GPT-6 Astra vs Qwen3.8 27B vs Claude Fable 5.1
Too close to call on our weighted score (GPT-6 Astra 68, Claude Fable 5.1 66, Qwen3.8 27B 66). The right pick depends on what you value most.
OpenAIGPT-6 Astra
68/100- ECI169.1
- Price$10.00 / $50.00
- Context1.05M
Alibaba (Qwen)Qwen3.8 27B
66/100- ECI151.8
- Price$0.40 / $2.55
- Context262K
AnthropicClaude Fable 5.1
66/100- ECI166.4
- Price$10.00 / $50.00
- Context1M
Too close to call
It is close. Our weighted score puts them within 2 points (GPT-6 Astra 68/100, Claude Fable 5.1 66/100, Qwen3.8 27B 66/100), so choose by what matters most for your work: GPT-6 Astra for raw capability and Qwen3.8 27B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT-6 AstraCapabilities Index (ECI): GPT-6 Astra 169.1 · Claude Fable 5.1 166.4 · Qwen3.8 27B 151.8
- Lowest priceQwen3.8 27BQwen3.8 27B $0.938 · GPT-6 Astra $20.00 · Claude Fable 5.1 $20.00 per 1M tokens (3:1 blend)
- Longest contextGPT-6 AstraGPT-6 Astra 1,050,000 · Claude Fable 5.1 1,000,000 · Qwen3.8 27B 262,144 tokens
- Widest inputsSame inputsGPT-6 Astra: Text, Images, PDFs · Qwen3.8 27B: Text, Images, Video · Claude Fable 5.1: Text, Images, PDFs
- Self-hostingQwen3.8 27BPublishes downloadable weights
| Measure | Weight | GPT-6 Astra | Qwen3.8 27B | Claude Fable 5.1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 100 | 79 | 97 |
| Price | 25% | 0 | 49 | 0 |
| Inputs & features | 15% | 80 | 70 | 80 |
| Context window | 10% | 61 | 37 | 60 |
| Overall | 100% | 68/100 | 66/100 | 66/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-6 AstraOpenAI | Qwen3.8 27BAlibaba (Qwen) | Claude Fable 5.1Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 169.1 (best) | 151.8 | 166.4 |
| ECI rank | #1 of 165 (best) | #54 of 165 | #6 of 165 |
| 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 | $0.40 (best) | $10.00 |
| Output | $50.00 | $2.55 (best) | $50.00 |
| Cached input | $1.00 | — | $0.25 (best) |
| Blended (3:1) | $20.00 | $0.938 (best) | $20.00 |
| Long-context rate | Over 272K: $20.00 / $75.00 | Same rate | Same rate |
| Price source | Official OpenAI API | Median of 49 providers | Official Anthropic API |
| Limits | |||
| Context window | 1,050,000 tokens (best) | 262,144 tokens | 1,000,000 tokens |
| Max output | 128,000 tokens (best) | 32,768 tokens | 128,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | Yes | No | Yes |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yeslow · medium · high · xhigh · max | Yes | Yeslow · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | gpt-6-astra | — | claude-fable-5-1 |
| API providers | 37 | 53 (best) | 37 |
| Released | Sep 4, 2026 | Aug 14, 2026 | Sep 1, 2026 |
| Knowledge cutoff | Apr 30, 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
- Qwen3.8 27B$9.10
- Claude Fable 5.1$200.00
Which should you choose?
Which is better: GPT-6 Astra, Qwen3.8 27B or Claude Fable 5.1?
It is close. Our weighted score puts them within 2 points (GPT-6 Astra 68/100, Claude Fable 5.1 66/100, Qwen3.8 27B 66/100), so choose by what matters most for your work: GPT-6 Astra for raw capability and Qwen3.8 27B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-6 Astra, Qwen3.8 27B or Claude Fable 5.1?
Qwen3.8 27B is cheaper at $0.40 input / $2.55 output per million tokens (median across 49 API providers). GPT-6 Astra costs $10.00 input / $50.00 output per million tokens (official OpenAI API price); Claude Fable 5.1 costs $10.00 input / $50.00 output per million tokens (official Anthropic API price). At a typical mix of three input tokens to one output token, that is $0.938 per million tokens for Qwen3.8 27B versus $20.00 for GPT-6 Astra (21× as much) and $20.00 for Claude Fable 5.1 (21× as much).
Which scores higher on benchmarks?
GPT-6 Astra scores higher on the Capabilities Index (ECI): GPT-6 Astra 169.1 (#1 of 165), Claude Fable 5.1 166.4 (#6 of 165) and Qwen3.8 27B 151.8 (#54 of 165). The confidence ranges of the top two overlap (165.9–174.3 vs 163.6–170.8), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-6 Astra, Qwen3.8 27B and Claude Fable 5.1 yet, so there is no like-for-like coding score. On overall capability, GPT-6 Astra leads, which tends to carry over to coding, but test on your own codebase. 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 262,144 for Qwen3.8 27B. Maximum output per response: GPT-6 Astra up to 128,000, Qwen3.8 27B up to 32,768, 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; Qwen3.8 27B accepts text, images and video; Claude Fable 5.1 accepts text, images and PDFs. They handle the same number of input types.
Are any of these open source?
Qwen3.8 27B publishes its weights and can be self-hosted; GPT-6 Astra and Claude Fable 5.1 is proprietary.
Which is newer?
GPT-6 Astra is the newest, released Sep 4, 2026. Claude Fable 5.1 came out Sep 1, 2026; Qwen3.8 27B came out Aug 14, 2026. Knowledge cutoff: GPT-6 Astra Apr 30, 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 Nov 2, 2026.
