GPT-6 Astra vs Claude Mythos 5 vs Claude Fable 5.1
Too close to call on our weighted score (GPT-6 Astra 36, Claude Mythos 5 36, Claude Fable 5.1 36). The right pick depends on what you value most.
OpenAIGPT-6 Astra
36/100- ECI166.5
- Price$10.00 / $50.00
- Context1.05M
AnthropicClaude Mythos 5
36/100- ECI—
- Price$10.00 / $50.00
- Context1M
- Anthropic
Claude Fable 5.1
36/100- ECI164.8
- Price$10.00 / $50.00
- Context1M
Too close to call
It is close. Our weighted score puts them within a point (GPT-6 Astra 36/100, Claude Mythos 5 36/100, Claude Fable 5.1 36/100), so choose by what matters most for your work: GPT-6 Astra on price and GPT-6 Astra for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceSame priceGPT-6 Astra $20.00 · Claude Mythos 5 $20.00 · Claude Fable 5.1 $20.00 per 1M tokens (3:1 blend)
- Longest contextGPT-6 AstraGPT-6 Astra 1,050,000 · Claude Mythos 5 1,000,000 · Claude Fable 5.1 1,000,000 tokens
- Widest inputsSame inputsGPT-6 Astra: Text, Images, PDFs · Claude Mythos 5: Text, Images, PDFs · Claude Fable 5.1: Text, Images, PDFs
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | GPT-6 Astra | Claude Mythos 5 | Claude Fable 5.1 |
|---|---|---|---|---|
| Price | 50% | 0 | 0 | 0 |
| Inputs & features | 30% | 80 | 80 | 80 |
| Context window | 20% | 61 | 60 | 60 |
| Overall | 100% | 36/100 | 36/100 | 36/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-6 AstraOpenAI | Claude Mythos 5Anthropic | Claude Fable 5.1Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 166.5 (best) | — | 164.8 |
| ECI rank | #2 of 148 (best) | — | #4 of 148 |
| GPQA DiamondGraduate-level science questions | 95.8% | — | — |
| FrontierMath Tiers 1–3Research-level mathematics | 93.7% (best) | — | 90.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 100% | — | 100% |
| SimpleQA VerifiedShort factual questions | 75.6% (best) | — | 70.8% |
| Price per million tokens | |||
| Input | $10.00 | $10.00 | $10.00 |
| Output | $50.00 | $50.00 | $50.00 |
| Cached input | $1.00 | — | $0.25 (best) |
| Blended (3:1) | $20.00 | $20.00 | $20.00 |
| Long-context rate | Over 272K: $20.00 / $75.00 | Same rate | Same rate |
| Price source | Official OpenAI API | Median of 2 providers | Official Anthropic API |
| Limits | |||
| Context window | 1,050,000 tokens (best) | 1,000,000 tokens | 1,000,000 tokens |
| Max output | 128,000 tokens | 128,000 tokens | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | Yes | Yes | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · medium · high · xhigh · max | Yes | Yeslow · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | gpt-6-astra | — | claude-fable-5-1 |
| API providers | 26 | 2 | 29 (best) |
| Released | Sep 4, 2026 | Jun 9, 2026 | Sep 1, 2026 |
| Knowledge cutoff | Apr 30, 2026 | Jan 31, 2026 | Jun 2026 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-6 Astra$200.00
- Claude Mythos 5$200.00
- Claude Fable 5.1$200.00
Which should you choose?
Which is better: GPT-6 Astra, Claude Mythos 5 or Claude Fable 5.1?
It is close. Our weighted score puts them within a point (GPT-6 Astra 36/100, Claude Mythos 5 36/100, Claude Fable 5.1 36/100), so choose by what matters most for your work: GPT-6 Astra on price and GPT-6 Astra for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GPT-6 Astra, Claude Mythos 5 or Claude Fable 5.1?
GPT-6 Astra, Claude Mythos 5 and Claude Fable 5.1 cost the same: $10.00 input / $50.00 output per million tokens.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-6 Astra has an ECI of 166.5, Claude Mythos 5 has not been scored yet and Claude Fable 5.1 has an ECI of 164.8.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-6 Astra, Claude Mythos 5 and Claude Fable 5.1 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
GPT-6 Astra has the largest context window at 1,050,000 tokens, against 1,000,000 for Claude Mythos 5 and 1,000,000 for Claude Fable 5.1. Maximum output per response: GPT-6 Astra up to 128,000, Claude Mythos 5 up to 128,000, Claude Fable 5.1 up to 128,000 tokens.
Which can read images, PDFs, audio or video?
GPT-6 Astra accepts text, images and PDFs; Claude Mythos 5 accepts text, images and PDFs; Claude Fable 5.1 accepts text, images and PDFs. They handle the same number of input types.
Are any of these open source?
No. GPT-6 Astra, Claude Mythos 5 and Claude Fable 5.1 are proprietary and only available through APIs and apps.
Which is newer?
GPT-6 Astra is the newest, released Sep 4, 2026. Claude Fable 5.1 came out Sep 1, 2026; Claude Mythos 5 came out Jun 9, 2026. Knowledge cutoff: GPT-6 Astra Apr 30, 2026, Claude Mythos 5 Jan 31, 2026, Claude Fable 5.1 Jun 2026.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
