# Gemini 4 Argon vs Claude Fable 5.2 vs GPT-6 Astra: Cost per Task

> Gemini 4 Argon's default passed 8 small tasks for $0.096 vs $0.41+ on the others. For hard or long exact work, start with Claude Fable 5.2 at medium.

- URL: https://blog.laozhang.ai/en/posts/gemini-4-argon-vs-claude-fable-5-2-vs-gpt-6-astra
- Published: 2026-11-11
- Updated: 2026-11-11
- Author: LaoZhang AI Team (https://blog.laozhang.ai/en/about)
- Topic: Model Comparisons
- Tags: Gemini 4 Argon, Claude Fable 5.2, GPT-6 Astra, AI model comparison, API pricing, Artificial Analysis

---
As of November 11, 2026, none of Gemini 4 Argon, Claude Fable 5.2 and GPT-6 Astra is the cheapest choice for every job, because the price per million tokens and the cost of getting one task right point in different directions. For small, well-specified work that a test or schema checks automatically, start with **Gemini 4 Argon (`gemini-4-argon`) at its default thinking level**. In a November 11, 2026 test of the same 8 tasks it passed all 11 graded answers for $0.096 at list price, while GPT-6 Astra needed $0.41 and Claude Fable 5.2 $0.42–$0.74 for the same passes. Keep Argon at its default, because at `high` the same answers cost 4 times as much.

For hard work, or anything that depends on reading a long input exactly, make **Claude Fable 5.2 (`claude-fable-5-2`) at `medium` effort** your default. On the Artificial Analysis Intelligence Index v5.0.1, as of November 11, 2026, it scores 56 for $3.80 per task, against 50 for $9.95 for Gemini 4 Argon (High), and in the same day's test it counted a 146 KB log exactly while Argon at its default skipped thinking and got every error count wrong. Send work to **GPT-6 Astra (`gpt-6-astra`)** when Fable 5.2 is ruled out, for example by high-risk security or biology topics or a zero-data-retention contract, or when you want the fastest correct answer on a long input: it got the same log right in 26 seconds for $0.82.

Two dates move these numbers. Fable 5.2 came out on November 9, 2026, and Argon's price per million tokens doubles on December 1, from $2/$10 to $4/$20 for input/output. At the new price Argon's default still passes the 8 tasks for the least money, but Argon at `high` becomes the most expensive of the seven settings tested.

## Which model and thinking level to start with, and when to switch

The right default depends on what a wrong answer costs you and whether you can catch it. If every answer passes through a test, a schema or a reviewer, a cheap model that sometimes fails is fine, because a failure costs one retry. If a wrong answer can slip through, pay for the model that gets it right the first time. The table pairs each kind of work with a first choice and the condition that should move you off it.

| Your work | Start with | Move when |
| --- | --- | --- |
| Small, well-specified tasks you check automatically: functions with tests, extraction into a schema, short summaries | Gemini 4 Argon at its default level (`medium`), the cheapest passing setting in the November 11 test | Answers keep failing the check: try Fable 5.2 at `low` or GPT-6 Astra at its default |
| Long documents or logs where every value must be exact | Claude Fable 5.2 at `medium`, or GPT-6 Astra at its default | Input above 272K tokens: Astra prices the whole request at $20/$75. Argon only at `high`, streamed, if you can wait minutes |
| Hard multi-step coding or analysis where a wrong answer is costly | Claude Fable 5.2 at `medium`, or `high` (its API default) for the hardest | Fable is blocked by topic, data-retention terms or forced tool choice: GPT-6 Astra at `high` |
| The highest independent score, cost secondary | Claude Fable 5.2 at `xhigh` or `max` | Cost matters: Fable `high` scores the same 62 as `xhigh` for $9.08 instead of $14.83 per task |
| Video or audio input | Gemini 4 Argon, the only one of the three that accepts it | No alternative among these three |

The scores and per-task costs in the table are Artificial Analysis Intelligence Index v5.0.1 figures as of November 11, 2026. The three vendors name their thinking controls differently and the same word does not buy the same amount of work: Argon has a "thinking level" with `low`, `medium` and `high`; Fable 5.2 has "effort" from `low` to `max`; Astra has "reasoning effort" from `low` to `max`. Argon's `medium` is not Fable's `medium`, so compare settings by results and cost, not by label.

![Decision flow: video or audio input goes to Gemini 4 Argon, work Fable 5.2 can't take goes to GPT-6 Astra, then small checked tasks go to Argon's default and long exact or hard work to Fable 5.2 at medium](https://blog.laozhang.ai/posts/en/gemini-4-argon-vs-claude-fable-5-2-vs-gpt-6-astra/img/which-model-to-start-with.webp)

For a closer look at two of the three, [Claude Fable 5.2 vs GPT-6 Astra: When Each Costs Less per Task](https://blog.laozhang.ai/en/posts/claude-fable-5-2-vs-gpt-6-astra) pairs every Fable and Astra effort level and tests both in their vendors' coding CLIs, and [Gemini 4 Argon vs GPT-6 Astra: Which to Use Now and After Dec 1](https://blog.laozhang.ai/en/posts/gemini-4-argon-vs-gpt-6-astra) covers caching and the December price in depth. If the real question is which Claude model, [Claude Fable 5.2 vs Opus 5.5: When Fable High Beats Opus Max](https://blog.laozhang.ai/en/posts/claude-fable-5-2-vs-opus-5-5) starts from the cheaper option Anthropic recommends.

## Same 8 tasks on all three: Argon's default passed at a quarter of the cost

On November 11, 2026, the same 8 tasks went to all three models at seven settings in total, and all 77 graded answers passed. The tasks were a word problem, three small Python functions graded by hidden tests, a 5-bullet summary of a 6,723-word article, an expression evaluator with 25 hidden tests, a CSV line parser with 16 hidden tests and a counting problem without code. The last three were asked twice, which makes 11 graded answers per setting.

Each request went through one laozhang.ai API key in the vendor's own request format: Gemini generateContent for Argon, not streamed, and Anthropic Messages for Fable 5.2 and OpenAI Chat Completions for Astra, both streamed, with no system instructions and no tools. "Default" means the thinking or effort field was left out, which per the vendors' docs is `medium` for Argon and `high` for Fable 5.2 in the API; OpenAI's docs don't state Astra's API default. Only responses that reported the requested model were counted, and those that did not were asked again. Costs are list prices applied to the token counts each response returned, not an invoice.

*Disclosure: [laozhang.ai is our own service](https://blog.laozhang.ai/en/about#laozhang). Its prices are platform pricing, not an independent comparison.*

| Setting | Passed | Input tokens | Output tokens (thinking, where reported) | Cost today | From December 1 | Cost vs Argon default, today / Dec 1 | Wall time |
| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |
| Gemini 4 Argon default (`medium`) | 11/11 | 12,712 | 7,085 (1,806) | $0.096 | $0.193 | 1.0x / 1.0x | 148 s |
| Gemini 4 Argon `high` | 11/11 | 12,712 | 36,291 (30,842) | $0.388 | $0.777 | 4.0x / 4.0x | 463 s |
| Claude Fable 5.2 `low` | 11/11 | 17,202 | 7,063 | $0.420 | $0.420 | 4.4x / 2.2x | 97 s |
| Claude Fable 5.2 `medium` | 11/11 | 17,202 | 9,317 | $0.510 | $0.510 | 5.3x / 2.7x | 122 s |
| Claude Fable 5.2 default (`high`) | 11/11 | 17,202 | 15,151 | $0.744 | $0.744 | 7.7x / 3.9x | 173 s |
| GPT-6 Astra default | 11/11 | 11,407 | 5,471 (1,964) | $0.413 | $0.413 | 4.3x / 2.1x | 92 s |
| GPT-6 Astra `high` | 11/11 | 11,407 | 9,455 (6,042) | $0.612 | $0.612 | 6.4x / 3.2x | 142 s |

Argon's default was the cheapest way through all 11 answers by a wide margin. The next cheapest settings, Astra at its default and Fable 5.2 at `low`, cost 4.3 and 4.4 times as much today and still about twice as much from December 1. Argon at `high` wrote 5.1 times the output of its default for the same passes, 30,842 tokens of it thinking, so today its $0.388 sits just under Astra's default and Fable's `low`, and from December 1 its $0.777 is above every other setting, Fable 5.2's default ($0.744) included. Fable 5.2 at `low` and Astra at its default cost the same within 2%, and within each vendor the step up is steep: Fable's default cost 1.77 times its `low`, and Astra's `high` 1.48 times its default.

![Cost of the same 8 tasks for all seven settings, today and from December 1: Gemini 4 Argon high rises from $0.388 to $0.777 and becomes the most expensive, while Argon's default stays cheapest at $0.193](https://blog.laozhang.ai/posts/en/gemini-4-argon-vs-claude-fable-5-2-vs-gpt-6-astra/img/eight-task-cost-today-vs-dec-1.webp)

You can redo any row from the prices further down. Argon's default is 12,712 × $2 + 7,085 × $10 per million tokens, or $0.096, and Fable `low` is 17,202 × $8 + 7,063 × $40, or $0.420. Astra's default is $0.413 rather than the $0.388 its plain input and output would give, because Astra wrote 10,104 tokens of the long article to cache without being asked, and OpenAI charges those writes at $12.50 instead of $10 per million. Fable 5.2 counts its thinking inside its output tokens and doesn't report it separately.

These are small, fully specified tasks that every setting passes, so the table shows what the same result costs in tokens, money and time. It does not show which model is more capable; for that, use the index and benchmark sections below. The limits are plain: one gateway, one day, one or two samples per cell, and whether a missing effort field maps to the documented default through a gateway can't be proven from the response. Argon was called without streaming and its repeated calls ran one after another, so treat its wall times as rough.

## Why the lowest token price is not always the cheapest result

Gemini 4 Argon's token price is a quarter of Fable 5.2's and a fifth of GPT-6 Astra's until November 30, yet at `high` it loses most of that advantage today and all of it from December 1, because thinking is billed as output and Argon at `high` thinks at length. The cost of one task is input tokens times the input price plus output tokens, thinking included, times the output price, so a model that writes ten times more output can overturn a price advantage of four or five times.

The 8-task test shows it directly: Argon at `high` wrote 36,291 output tokens against 7,085 at its default, so the same 11 passes cost 4 times as much. On the 146 KB log task described in the next section, Argon at `high` produced 44,980 output tokens, 44,895 of them thinking, against 8,156 for Fable 5.2 at `medium` and 2,423 for Astra at its default. Uncached, that comes to about $0.61 for Argon today, still below Astra's $0.82 and Fable's $0.95, but about $1.22 from December 1, the most of the three. Artificial Analysis shows the same pattern at scale: in v5.0.1 as of November 11, 2026, Gemini 4 Argon (High) writes 116K output tokens per index task, 68.3K of them reasoning, while GPT-6 Astra at `max` writes 52K, so Astra's higher token price buys a cheaper task ($6.24 against $9.95).

The practical rule is to compare settings at an equal result, never at an equal label or an equal token price. When a comparison calls one model "40% cheaper", check whether that means per million tokens or per finished task, and which index version the per-task figure comes from.

## A 146 KB log count: Argon's default was fast and wrong

On a long input where every number has to be exact, Gemini 4 Argon at its default thinking level gave a confident wrong answer in 22 seconds, while Fable 5.2 and GPT-6 Astra at their defaults were right. The test, run on November 11, 2026, used a generated application log of 146 KB: 1,500 lines with 326 ERROR lines, plus WARN lines carrying the same error codes as distractors. The model had to report 7 values, such as the count of each ERROR code and the number of distinct users hitting E503, and the answer key was written before any call.

| Setting | All 7 values right | Time | Output tokens (thinking) | List-price cost today | From December 1 |
| --- | --- | ---: | ---: | ---: | ---: |
| Gemini 4 Argon default | No | 22 s | 85 (0) | $0.16 | $0.32 |
| Gemini 4 Argon `high`, streamed | Yes | 329 s | 44,980 (44,895) | $0.46 (about $0.61 uncached) | about $1.22 uncached |
| Claude Fable 5.2 default (`high`) | Yes | 74 s | 9,792 | $1.02 | $1.02 |
| Claude Fable 5.2 `medium` | Yes | 64 s | 8,156 | $0.95 | $0.95 |
| GPT-6 Astra default | Yes | 26 s | 2,423 (2,374) | $0.82 | $0.82 |

Argon at its default spent zero thinking tokens and wrote 85 output tokens. All five ERROR counts came back 18–38% too low (E503, for instance, 58 instead of 94), the E503 user count was 46 instead of 78, and only the request ID with the most retries was right. The same input produced the same pattern on November 4, 2026, when Argon's default was wrong in both of two tries, again with no thinking tokens. At `high` Argon got everything right, but its first answer text arrived after 325 seconds; the $0.46 is lower than the uncached figure because it read 77,258 input tokens from Google's implicit cache left by the default call a few minutes earlier.

Run Argon at `high` with streaming. In the November 4 run, a non-streaming Argon `high` call stopped at 32,768 output tokens while still thinking and returned no answer, after $0.49 of tokens at list price; the two streamed calls that day were right in 271–402 seconds. Astra's $0.82 includes writing the whole 55,841-token input to cache at $12.50 per million, which OpenAI does by default; a repeat within 30 minutes would read it at $1 instead. This is one task type with one sample per setting today, so treat it as a warning about exact counting over long inputs, not as a general ranking.

## Artificial Analysis index: cheaper settings outscore Argon (High)

On the Artificial Analysis Intelligence Index v5.0.1, as of November 11, 2026, Gemini 4 Argon (High) scores 50 at $9.95 per task, and every Fable 5.2 and GPT-6 Astra setting except Astra `low` scores higher, most of them for less money. Artificial Analysis publishes Argon only at High, which is not its API default, and prices it at the December rate of $4/$20. It publishes five levels each for Fable 5.2 and Astra.

| Model and setting | Index score | Cost per index task | Minutes per task |
| --- | ---: | ---: | ---: |
| Claude Fable 5.2 `max` | 65 | $26.54 | 44.4 |
| Claude Fable 5.2 `xhigh` | 62 | $14.83 | 26.1 |
| Claude Fable 5.2 `high` | 62 | $9.08 | 17.5 |
| GPT-6 Astra `max` | 57 | $6.24 | 23.0 |
| Claude Fable 5.2 `medium` | 56 | $3.80 | 7.9 |
| GPT-6 Astra `xhigh` | 55 | $3.66 | 11.4 |
| GPT-6 Astra `high` | 55 | $3.24 | 9.8 |
| Claude Fable 5.2 `low` | 54 | $3.10 | 6.4 |
| GPT-6 Astra `medium` | 52 | $2.73 | 7.1 |
| Gemini 4 Argon (High) | 50 | $9.95 | 28.6 |
| GPT-6 Astra `low` | 43 | $1.42 | 3.8 |

*Artificial Analysis Intelligence Index v5.0.1, as of November 11, 2026, at API list prices; Argon (High) at $4/$20. Data from the [Artificial Analysis Gemini 4 Argon vs Claude Fable 5.2 comparison](https://artificialanalysis.ai/models/comparisons/gemini-4-argon-vs-claude-fable-5-2) and the [Artificial Analysis GPT-6 Astra vs Claude Fable 5.2 release comparison](https://artificialanalysis.ai/models/releases/comparisons/gpt-6-astra-vs-claude-fable-5-2).*

Artificial Analysis changed Argon's figure without a note. On November 4, 2026 the same pages showed Gemini 4 Argon (High) at 50 for $7.68 per task; on November 11 they show 50 for $9.95 and 28.6 minutes, still under v5.0.1 and still at $4/$20. The [Gemini 4 Argon vs GPT-6 Astra page](https://blog.laozhang.ai/en/posts/gemini-4-argon-vs-gpt-6-astra) quotes the November 4 reading. At today's $2/$10, halving every price component gives $4.98 per task through November 30; that is arithmetic on the AA figure, not a number Artificial Analysis publishes.

Read at equal score, the table favors the other two. Even against that $4.98, five settings score higher and cost less per task: Astra `medium` (52, $2.73), Fable 5.2 `low` (54, $3.10), Astra `high` (55, $3.24), Astra `xhigh` (55, $3.66) and Fable 5.2 `medium` (56, $3.80). From December 1 Argon (High) at $9.95 also costs more than Astra `max` (57, $6.24) and Fable 5.2 `high` (62, $9.08). The index has no row for Argon at `medium` or `low`, so it says nothing about the cheap default the decision table recommends for checked work.

Between Fable 5.2 and Astra, Fable `medium` scores one point under Astra `max` for 39% less money and about a third of the time, and Fable `low` sits one point under Astra `high` and `xhigh` for 4–15% less. Above 57 only Fable has rows, and its top setting is expensive: Fable `max` at $26.54 costs 4.25 times Astra `max`. The ranking that circulated after launch, Fable 5.2 ahead of Gemini 4 Carbon ahead of GPT-6 Astra, comes from each model's top published setting on this one index (65 for Fable `max`, 60 for Carbon (High), 57 for Astra `max`); at `medium`, Fable scores 56 and drops below Astra's top setting. An index of nine evaluations is still not your task.

## Which is best for coding? Each benchmark gives a different answer

Claude Fable 5.2 leads the coding benchmarks that include it, but its lead over GPT-6 Astra is small on terminal work and large on harder code generation, and Gemini 4 Argon comes first only on agentic automation. Artificial Analysis's sub-evaluations in v5.0.1, as of November 11, 2026, compare Gemini 4 Argon (High) with Fable 5.2 and Astra at `max`: Terminal-Bench 4.0 is 57.1%, 59.6% and 59.1%, almost a tie; FrontierCode-AA 2.0 is 25.7%, 48.8% and 42.4%, a wide gap; and AutomationBench-AA is 77.5%, 76.4% and 68.5%, the one row where Argon is first. At lower Fable and Astra settings these gaps shrink.

The vendors' own launch tables never put all three side by side. Google's Argon table from September 30, 2026 compared it with GPT-6 Astra, Fable 5.1 and Opus 5.5, and put Argon ahead of Astra on 14 of 19 rows, including DeepSWE v1.1 (77.9 vs 74.1) and the 256K–1M long-context GraphWalks test (84.2 vs 71.8), while Astra led on FrontierSWE v2 (65.5 vs 55.0) and Terminal-Bench 4.0. [Anthropic's Fable 5.2 announcement](https://www.anthropic.com/claude-fable-and-mythos-5-2) of November 9 compares Fable 5.2 with Astra and with Gemini 4 Carbon, not Argon, and puts Fable first on FrontierSWE v2 at 69.9% against 65.5% for Astra. Combining the two tables on FrontierSWE v2 gives roughly Fable 5.2 69.9, Astra 65.5 and Argon 55.0, but those are two vendors' runs with their own settings, so read it as a direction, not a margin. OpenAI has published no table against Fable 5.2 or Argon.

For a coding agent, that points to Fable 5.2 when the code is hard to get right, Astra as a close second on terminal-heavy work, and Argon when the work is closer to multi-step automation, or when inputs include video or audio, which only Argon accepts among the three.

## API prices per million tokens and what changes on December 1

Per million tokens, Gemini 4 Argon is the cheapest of the three until November 30, 2026, and still the cheapest from December 1 at twice today's price. The table lists the direct-API rates as of November 11, 2026, from [Google's Gemini API pricing page](https://aistudio.google.com/docs/pricing), [Anthropic's Claude pricing page](https://platform.claude.com/docs/en/about-claude/pricing) and [OpenAI's API pricing page](https://developers.openai.com/api/docs/pricing).

| Model (API ID) | Input | Output, thinking included | Cache read | Cache write and other rules |
| --- | ---: | ---: | ---: | --- |
| Gemini 4 Argon (`gemini-4-argon`), through November 30 | $2.00 | $10.00 | $0.10 | Implicit caching only; no free API tier |
| Gemini 4 Argon, from December 1 | $4.00 | $20.00 | $0.20 | Same rules |
| Claude Fable 5.2 (`claude-fable-5-2`) | $8.00 | $40.00 | $0.20 | $10 for a 5-minute write, $16 for 1 hour; flat across the 1M context; Batch $4/$20 |
| GPT-6 Astra (`gpt-6-astra`), up to 272K input tokens | $10.00 | $50.00 | $1.00 | $12.50 for the default 30-minute write, $25 for 12 hours; above 272K input the whole request is $20/$75; Batch half price |

Per token, then, Argon costs a quarter of Fable 5.2 and a fifth of Astra until November 30, and a half and two fifths from December 1. The thing to recheck on December 1 is any job where you run Argon at `high`: on the 8 small tasks it moves from just under Astra's default to the most expensive of the seven settings tested, and on the long log its uncached cost passes both Fable 5.2 at `medium` and Astra at its default. Argon's default stays the cheapest passing setting on the 8 tasks at the new price. The Artificial Analysis cost for Argon (High) doesn't move, because it is already computed at $4/$20.

The three API IDs are the full names: `gemini-4-argon` has been generally available since October 13, 2026, `claude-fable-5-2` since November 9 (Fable 5.1 is now a legacy model), and `gpt-6-astra` since September 3, still OpenAI's top general model. "Astra 6" in searches means GPT-6 Astra, and there is no Fable 5.5 or GPT-6.2. If you are still on Fable 5.1, [Fable 5.1 vs 5.2: Cheaper at Medium, Costlier at High and Max](https://blog.laozhang.ai/en/posts/claude-fable-5-1-vs-5-2) covers the switch, and [GPT-6 Astra API Pricing: $10/$50 per 1M Tokens, Caching Costs](https://blog.laozhang.ai/en/posts/gpt-6-astra-api-pricing) explains Astra's 272K step, cache tiers and Batch.

## Does the same input cost the same? Fable 5.2 counts more tokens

No. The three tokenizers count the same text differently, so the price per million tokens understates Fable 5.2's input cost on English text. On November 11, 2026, the 6,723-word English article from the 8-task test counted as 11,110 tokens on Gemini 4 Argon, 15,335 on Fable 5.2 and 10,106 on GPT-6 Astra, so Fable counts 1.52 times as many tokens as Astra and Argon 1.10 times. The 146 KB log counted as 79,312 tokens on Gemini 4 Argon, 78,537 on Fable 5.2 and 55,843 on Astra, so on logs and IDs Argon also counts about 1.4 times as many tokens as Astra.

| Model | Tokens for the 6,723-word article | Tokens for the 146 KB log | Log input at list price | Tokens for a short English passage (Astra = 1.00) | English input cost relative to Astra |
| --- | ---: | ---: | ---: | ---: | ---: |
| Gemini 4 Argon | 11,110 | 79,312 | $0.159, $0.317 from December 1 | 1.00 | 0.20, 0.40 from December 1 |
| Claude Fable 5.2 | 15,335 | 78,537 | $0.628 | 1.45 | 1.16 |
| GPT-6 Astra | 10,106 | 55,843 | $0.558 | 1.00 | 1.00 |

The last column is the token ratio times the input price ratio: for Fable 5.2, 1.45 × $8 / $10 = 1.16, so the same English input costs about 16% more on Fable than on Astra despite Fable's lower price. Argon's advantage survives the tokenizer because its price gap is much wider. The passage ratios come from one short passage per language, measured for Fable on Fable 5.1 on October 26, 2026 (Anthropic says 5.2 uses the same tokenizer, and the article count matched 5.1's exactly) and for Argon on November 4. In other languages Fable's input costs between 0.98 times Astra's (Japanese) and 1.52 times (Korean), and Argon's stays between 0.13 and 0.20 times Astra's until November 30. Astra's log figure is its plain input rate; with its default cache write the same input actually cost $0.698.

## Should you use Gemini 4 Carbon instead of Argon?

Gemini 4 Carbon (`gemini-4-carbon`) has been generally available since November 10, 2026, on the paid Gemini API and in Google AI Studio, at the same per-token price as Argon, including the introductory $2/$10 through November 30, with the same 1M context and the same `low`/`medium`/`high` thinking levels. Google's [Gemini API changelog](https://aistudio.google.com/docs/changelog) calls it "our most capable reasoning model", and its [Gemini 4 Carbon notes](https://aistudio.google.com/docs/whats-new-gemini-4-carbon) say Carbon "spends more thinking tokens per turn to explore thoroughly and verify its work, which increases per-turn token use and latency compared to Argon", while Argon suits most software engineering and knowledge work with faster responses and lower token spend per turn.

On the Artificial Analysis Intelligence Index v5.0.1, as of November 11, 2026, Gemini 4 Carbon (High) scores 60 for $7.76 per task, higher and cheaper per task than Argon (High) at 50 for $9.95, both priced at $4/$20. That makes Carbon worth a trial wherever you were about to run Argon at `high`, but it was not part of the November 11 test because the gateway used for it did not offer `gemini-4-carbon` that day. Anthropic's Fable 5.2 launch table compares Fable with Carbon directly and puts Carbon ahead on AutomationBench (53.9% vs 51.4%) and behind on FrontierSWE v2 (61.2% vs 69.9%), on Anthropic's own runs.

## What rules a model out before quality matters

Before any score matters, check whether your requests can run on the model at all. All but the advice on streaming Argon come from each vendor's documentation as of November 2026, and any one of them can settle the choice.

- If your inputs include video or audio, only Gemini 4 Argon accepts them; Fable 5.2 and Astra take text and images.
- If your work involves high-risk cybersecurity or most research biology, Fable 5.2's safety classifiers will block much of it.
- If your contract requires zero data retention, Fable 5.2 needs 30-day retention unless Anthropic has authorized your organization.
- If your code forces a specific tool call, Fable 5.2 returns a 400 error; if it sets `temperature`, `top_p` or `top_k`, those must be removed for Fable 5.2, and its thinking cannot be turned off.
- If your code calls Astra through Chat Completions and needs function calling, move it to the Responses API, the only place Astra supports it.
- If single requests exceed 272K input tokens, Astra prices the whole request at $20/$75 per million.
- If you need Argon at `high`, stream the response: in the November 4, 2026 test a non-streaming `high` call stopped at 32,768 output tokens with no answer, even though Google's model page gives 65,536 as the default synchronous maximum. Also plan for explicit context caching being unavailable, because Argon caches implicitly only.
- If your team is in Russia or mainland China, none of the three vendors lists it among supported countries for its API; Google and OpenAI also leave out Belarus and Hong Kong.

## Redo the comparison on your own tasks

A dozen or two of your own tasks with an automatic check will tell you more than any index, and it takes an afternoon. The point is to compare settings at an equal pass rate and to keep the price per token and the cost per task apart.

1. Pick 10–20 real tasks whose answers a script can check: hidden tests, a JSON schema, or a known count. Include at least one long input with an exact answer, because that is where Argon's default failed.
2. Run each model at two or three settings: Argon at its default and `high`, streamed; Fable 5.2 at `low`, `medium` and its default; Astra at its default and `high`.
3. Save the usage block of every response: input, output, cached and cache-write tokens, and thinking tokens where the API reports them separately (Fable 5.2 folds them into its output count).
4. Compute each task's cost as input × input price + output × output price, plus cache reads and writes at their own rates, then compute Argon a second time at $4/$20.
5. Divide the total cost of each setting by the number of tasks it passed, and pick the cheapest setting that clears your bar, not the cheapest per token.

Repeat the run on December 1 for anything that uses Argon, and whenever a vendor ships a new version: Fable 5.2 has already made 5.1 a legacy model, and Artificial Analysis revised Argon's cost between two reads a week apart.

## Fable 5.2, Argon and Astra on a subscription instead of the API

On a subscription the three are reached through different plans, and the allowances change often. Claude Fable 5.2 is on every paid Claude plan but not Free; in Claude Code you switch to it with `/model fable`, where it defaults to `medium` effort. Max, Ultra and premium seats include it up to 50% of the weekly limit, while Pro and standard Team seats pay with usage credits once the launch week ends on November 16, 2026, at 11:59 pm PT. Gemini 4 Argon is in the Gemini app and AI Mode on Google AI Pro and Ultra and in Antigravity on paid plans, not on the free Gemini app tier. GPT-6 Astra is reported to give Plus and standard Business users roughly 5–45 messages per 5 hours in Work and Codex, with no 5-hour limit on Pro, while Free and Go get GPT-6 Luna instead.

For the Google side, [How to Access Gemini 4 Argon for Free: Who Actually Qualifies](https://blog.laozhang.ai/en/posts/how-to-access-gemini-4-argon) covers the ways to use Argon at no cost, and [Gemini 4 Argon Usage Limits: Pro vs Ultra, Resets, API Credits](https://blog.laozhang.ai/en/posts/gemini-4-argon-usage-limits) covers what each Google plan allows.

## Sources

External pages this guide links to, in the order they appear. Last updated 2026-11-11.

- [Artificial Analysis Gemini 4 Argon vs Claude Fable 5.2 comparison](https://artificialanalysis.ai/models/comparisons/gemini-4-argon-vs-claude-fable-5-2) (artificialanalysis.ai)
- [Artificial Analysis GPT-6 Astra vs Claude Fable 5.2 release comparison](https://artificialanalysis.ai/models/releases/comparisons/gpt-6-astra-vs-claude-fable-5-2) (artificialanalysis.ai)
- [Anthropic's Fable 5.2 announcement](https://www.anthropic.com/claude-fable-and-mythos-5-2) (anthropic.com)
- [Google's Gemini API pricing page](https://aistudio.google.com/docs/pricing) (aistudio.google.com)
- [Anthropic's Claude pricing page](https://platform.claude.com/docs/en/about-claude/pricing) (platform.claude.com)
- [OpenAI's API pricing page](https://developers.openai.com/api/docs/pricing) (developers.openai.com)
- [Gemini API changelog](https://aistudio.google.com/docs/changelog) (aistudio.google.com)
- [Gemini 4 Carbon notes](https://aistudio.google.com/docs/whats-new-gemini-4-carbon) (aistudio.google.com)