Claude Fable 5.2 vs GPT-6 Astra
GPT-6 Astra is OpenAI’s flagship, released in September 2026; Claude Fable 5.2 is Anthropic’s top generally available model, released on 9 November 2026. Both are reasoning models with a context window of about a million tokens and up to 128,000 output tokens, and both are pitched at long agent work. This page lines up what third parties measured, what each company published, and what a task costs.
We run apimodels.app, which sells both models below list price. We did not run these benchmarks; the scores come from Artificial Analysis, Vals AI, Anthropic and OpenAI, with links at the end of the page.
Claude Fable 5.2
Wins most of the tests
- ·Artificial Analysis Intelligence Index 65 (Astra 57)
- ·Vals Index 71.31%, first of 36 (Astra 65.02%, seventh)
- ·FrontierCode-AA 48.8% vs 42.4%; HLE-Diamond 80.2% vs 63.8%
- ·About $15.92 per Artificial Analysis task at our prices
GPT-6 Astra
Fewer tokens, lower bill
- ·52,000 output tokens per task against 274,000
- ·About $2.12 per Artificial Analysis task at our prices
- ·Wins GDP.pdf (31.0% vs 24.2%)
- ·OpenAI reports 96.3% on MRCR v2 at 512K–1M tokens
Pick Claude Fable 5.2 for the hardest coding, research and agent work. Pick GPT-6 Astra when cost per task, document output or very long inputs matter more than the last few points.
Independent benchmarks
Fable 5.2 leads on ten of the eleven numbers below. The gaps are largest on knowledge and science: 80.2% against 63.8% on HLE-Diamond and 67 against 42 on AA-Omniscience, which penalises wrong guesses. Astra’s one independent win is GDP.pdf, a test of producing documents, at 31.0% against 24.2%. On Terminal-Bench 4.0 the two are within half a point. Artificial Analysis ran both at max effort, with Anthropic’s default fallback on for Fable 5.2.
| Benchmark | Run by | Claude Fable 5.2 | GPT-6 Astra |
|---|---|---|---|
| Intelligence Index v5.0.1 | Artificial Analysis | 65 | 57 |
| Vals Index v2.2 | Vals AI | 71.31% (1st of 36) | 65.02% (7th) |
| HLE-Diamond | Artificial Analysis | 80.2% | 63.8% |
| CritPt v2 (hard subset) | Artificial Analysis | 76.5% | 70.5% |
| Terminal-Bench-Science 0.1 | Artificial Analysis | 73.3% | 63.3% |
| FrontierCode-AA 2.0 | Artificial Analysis | 48.8% | 42.4% |
| AA-Omniscience v1.1 | Artificial Analysis | 67 | 42 |
| AA-Briefcase v1.1 (Elo) | Artificial Analysis | 1785 | 1569 |
| AutomationBench-AA | Artificial Analysis | 76.4% | 68.5% |
| Terminal-Bench 4.0 | Artificial Analysis | 59.6% | 59.1% |
| GDP.pdf | Artificial Analysis | 24.2% | 31.0% |
What Anthropic and OpenAI report
Anthropic’s launch table includes Astra and puts Fable 5.2 ahead on every row they share, with the largest gaps on Remote Labor Index (41.3% against 20.8%) and Humanity’s Last Exam with tools (74.2% against 57.2%). OpenAI’s own September figures for Astra highlight what Anthropic’s table does not cover: 96.3% on its MRCR v2 long-context retrieval test in the 512K to 1M token band, 97.6% on FrontierMath Tier 4 and 96.0% on GPQA Diamond, and 72.6% on OSWorld 2.0 for computer use. Astra’s computer-use tool is not available through apimodels.app, so that last result does not carry over to calls made here.
| Benchmark (Anthropic-reported) | Claude Fable 5.2 | GPT-6 Astra |
|---|---|---|
| FrontierCode 2.0 | 49.1% (xhigh) | 42.8% |
| FrontierSWE v2 | 69.9% | 65.5% |
| AutomationBench | 51.4% | 41.4% |
| Terminal-Bench-Science 0.1 | 74.6% | 68.1% |
| OSWorld 2.1 (offline set, partial credit) | 89.3% | 79.5% |
| Remote Labor Index | 41.3% (high) | 20.8% |
| Humanity’s Last Exam (with tools) | 74.2% | 57.2% |
| Chartography (no tools) | 73.4% | 71.0% |
Price and cost per task
By list price Astra is the dearer model per token, $10 / $50 against $8 / $40. Per task it is the reverse, because Astra writes far less: Artificial Analysis counted 52,000 output tokens per task for Astra, 35,800 of them reasoning, against 274,000 and 193,000 for Fable 5.2. At list prices that made a task $6.24 on Astra and $26.54 on Fable 5.2. Vals, on its own task set, reports the opposite order, $21.28 per test for Astra and $17.94 for Fable 5.2, so the gap depends heavily on the work.
On apimodels.app, Astra is 66% below OpenAI’s list and Fable 5.2 is 40% below Anthropic’s, on every token type, so the Artificial Analysis task costs scale to about $2.12 for Astra and about $15.92 for Fable 5.2. One Astra rule to budget for: requests above 272K input tokens bill at twice the input price and 1.5 times the output price.
| Per 1M tokens | Fable 5.2 list | Fable 5.2 on apimodels.app | Astra list | Astra on apimodels.app |
|---|---|---|---|---|
| Input | $8 | $4.80 | $10 | $3.40 |
| Output | $40 | $24 | $50 | $17 |
| Cached input | $0.20 | $0.12 | $1.00 | $0.34 |
Speed
Both take about four minutes to the first answer token at max effort. Fable 5.2 then streams more than twice as fast and finishes a single request sooner, while Astra finishes whole tasks in about half the time because it writes so much less.
| Measured by Artificial Analysis | Claude Fable 5.2 | GPT-6 Astra |
|---|---|---|
| Output speed | 101 tokens/s | 38 tokens/s |
| Time to first answer token | 247.85 s | 249.02 s |
| End-to-end time, single request (mean) | 435.62 s | 581.42 s |
| Time per index task | 45.0 min | 23.0 min |
Which to use
Hard coding, research, science and multi-app automation: Claude Fable 5.2. It leads on FrontierCode, Terminal-Bench-Science, CritPt, HLE-Diamond and AutomationBench, and on Anthropic’s Remote Labor Index by about double.
Reports, documents and spreadsheets as the output: GPT-6 Astra, which wins GDP.pdf.
Retrieval across very long inputs: GPT-6 Astra by OpenAI’s figures, but budget for the higher rate above 272K input tokens.
Volume, or a fixed budget per task: GPT-6 Astra, at about an eighth of Fable 5.2’s cost per Artificial Analysis task on our prices.
Security research: GPT-6 Astra is the more usable of the two, since Fable 5.2’s safeguards refuse most high-risk cyber requests; OpenAI reports 100% on its ExploitBench for Astra.
Both models are on one apimodels.app key through the OpenAI-compatible /v1/chat/completions, so routing the hard tasks to Fable 5.2 and the rest to Astra is a change of the model string.
Caveats
Artificial Analysis ran Fable 5.2 with Anthropic’s fallback switched on, so a small share of blocked requests were answered by another model. OpenAI’s and Anthropic’s figures are their own and have not been reproduced. Astra’s computer-use tool is not available through apimodels.app. Fable 5.2 is two months newer than Astra, and OpenAI usually answers a rival release within weeks, so expect this comparison to need an update.
Sources
Independent scores, speeds and task costs: Artificial Analysis and Vals AI, read on 12 November 2026. Vendor figures: Anthropic’s Fable 5.2 announcement and OpenAI’s GPT-6 Astra launch figures. apimodels.app prices are our own.
Quick start
cURL
curl https://api.apimodels.app/v1/chat/completions \
-H "Authorization: Bearer $APIMODELS_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-6-astra",
"messages": [{"role": "user", "content": "Summarise the attached quarterly report into a one-page brief: ..."}]
}'
# same key, same endpoint: change "model" to "claude-fable-5-2" to run Fable 5.2FAQ
Is Claude Fable 5.2 better than GPT-6 Astra?
Claude Fable 5.2 scores higher on almost every independent test: 65 against 57 on the Artificial Analysis Intelligence Index, and first against seventh on the Vals Index. GPT-6 Astra wins on document output (GDP.pdf) and, by OpenAI’s figures, on long-context retrieval, and it costs much less per task.
Which costs less to run, Fable 5.2 or GPT-6 Astra?
GPT-6 Astra usually costs less per task even though its list price per token is higher, because it writes far fewer tokens: 52,000 output tokens per Artificial Analysis task against 274,000. At apimodels.app prices that works out at about $2.12 per task for Astra and about $15.92 for Fable 5.2. On Vals’s task set the order flips, so test on your own work.
Which is faster, Fable 5.2 or GPT-6 Astra?
Fable 5.2 streams faster, 101 against 38 tokens per second, and finishes a single request sooner. GPT-6 Astra finishes whole tasks faster, 23 minutes against 45 per Artificial Analysis task, because it writes far less. Both took about four minutes to the first answer token at max effort.
Is there a GPT-6.1 Astra to compare instead?
GPT-6.1 Astra does not exist. OpenAI released only GPT-6.1 Sol on 29 September 2026 and cancelled the planned GPT-6.1 Astra, so GPT-6 Astra remains OpenAI’s top model and the right comparison for Fable 5.2.
Can I use both models with one API key?
Both models run on one apimodels.app key. Call POST https://api.apimodels.app/v1/chat/completions with model gpt-6-astra or claude-fable-5-2; Astra also works on /v1/responses and Fable 5.2 on the native /v1/messages. There is one balance, no OpenAI organization verification and no Anthropic account.
Related
- Claude Fable 5.2 model page
- GPT-6 Astra model page
- Claude Fable 5.2 vs Gemini 4 Carbon
- Fable 5.2 vs Opus 5.5
- GPT-6 Astra API tested
