Claude Fable 5.1 vs Claude Mythos 5 vs GPT-6 Astra
Too close to call on our weighted score (GPT-6 Astra 36, Claude Fable 5.1 36, Claude Mythos 5 36). The right pick depends on what you value most.
AnthropicClaude Fable 5.1
36/100- ECI164.8
- Price$10.00 / $50.00
- Context1M
- Anthropic
Claude Mythos 5
36/100- ECI—
- Price$10.00 / $50.00
- Context1M
OpenAIGPT-6 Astra
36/100- ECI166.5
- Price$10.00 / $50.00
- Context1.05M
Too close to call
It is close. Our weighted score puts them within a point (GPT-6 Astra 36/100, Claude Fable 5.1 36/100, Claude Mythos 5 36/100), so choose by what matters most for your work: Claude Fable 5.1 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 priceClaude Fable 5.1 $20.00 · Claude Mythos 5 $20.00 · GPT-6 Astra $20.00 per 1M tokens (3:1 blend)
- Longest contextGPT-6 AstraGPT-6 Astra 1,050,000 · Claude Fable 5.1 1,000,000 · Claude Mythos 5 1,000,000 tokens
- Widest inputsSame inputsClaude Fable 5.1: Text, Images, PDFs · Claude Mythos 5: Text, Images, PDFs · GPT-6 Astra: Text, Images, PDFs
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | Claude Fable 5.1 | Claude Mythos 5 | GPT-6 Astra |
|---|---|---|---|---|
| Price | 50% | 0 | 0 | 0 |
| Inputs & features | 30% | 80 | 80 | 80 |
| Context window | 20% | 60 | 60 | 61 |
| 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 | Claude Fable 5.1Anthropic | Claude Mythos 5Anthropic | GPT-6 AstraOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 164.8 | — | 166.5 (best) |
| ECI rank | #4 of 148 | — | #2 of 148 (best) |
| GPQA DiamondGraduate-level science questions | — | — | 95.8% |
| FrontierMath Tiers 1–3Research-level mathematics | 90.2% | — | 93.7% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 100% | — | 100% |
| SimpleQA VerifiedShort factual questions | 70.8% | — | 75.6% (best) |
| Price per million tokens | |||
| Input | $10.00 | $10.00 | $10.00 |
| Output | $50.00 | $50.00 | $50.00 |
| Cached input | $0.25 (best) | — | $1.00 |
| Blended (3:1) | $20.00 | $20.00 | $20.00 |
| Long-context rate | Same rate | Same rate | Over 272K: $20.00 / $75.00 |
| Price source | Official Anthropic API | Median of 2 providers | Official OpenAI API |
| Limits | |||
| Context window | 1,000,000 tokens | 1,000,000 tokens | 1,050,000 tokens (best) |
| 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 | claude-fable-5-1 | — | gpt-6-astra |
| API providers | 29 (best) | 2 | 26 |
| Released | Sep 1, 2026 | Jun 9, 2026 | Sep 4, 2026 |
| Knowledge cutoff | Jun 2026 | Jan 31, 2026 | Apr 30, 2026 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Claude Fable 5.1$200.00
- Claude Mythos 5$200.00
- GPT-6 Astra$200.00
Which should you choose?
Which is better: Claude Fable 5.1, Claude Mythos 5 or GPT-6 Astra?
It is close. Our weighted score puts them within a point (GPT-6 Astra 36/100, Claude Fable 5.1 36/100, Claude Mythos 5 36/100), so choose by what matters most for your work: Claude Fable 5.1 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, Claude Fable 5.1, Claude Mythos 5 or GPT-6 Astra?
Claude Fable 5.1, Claude Mythos 5 and GPT-6 Astra 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. Claude Fable 5.1 has an ECI of 164.8, Claude Mythos 5 has not been scored yet and GPT-6 Astra has an ECI of 166.5.
Which is better for coding?
There are no published SWE-bench Verified results for Claude Fable 5.1, Claude Mythos 5 and GPT-6 Astra 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 Fable 5.1 and 1,000,000 for Claude Mythos 5. Maximum output per response: Claude Fable 5.1 up to 128,000, Claude Mythos 5 up to 128,000, GPT-6 Astra up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Claude Fable 5.1 accepts text, images and PDFs; Claude Mythos 5 accepts text, images and PDFs; GPT-6 Astra accepts text, images and PDFs. They handle the same number of input types.
Are any of these open source?
No. Claude Fable 5.1, Claude Mythos 5 and GPT-6 Astra 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: Claude Fable 5.1 Jun 2026, Claude Mythos 5 Jan 31, 2026, GPT-6 Astra Apr 30, 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.
