GPT-6 Astra vs Claude Opus 5.5 vs Qwen 3.8 Max Prime
Too close to call on our weighted score (Qwen 3.8 Max Prime 42, Claude Opus 5.5 39, GPT-6 Astra 36). The right pick depends on what you value most.
OpenAI
GPT-6 Astra
36/100- ECI166.5
- Price$10.00 / $50.00
- Context1.05M
Anthropic
Claude Opus 5.5
39/100- ECI167.4
- Price$4.00 / $20.00
- Context1M
Alibaba (Qwen)
Qwen 3.8 Max Prime
42/100- ECI—
- Price$4.00 / $12.00
- Context1M
Too close to call
It is close. Our weighted score puts them within 3 points (Qwen 3.8 Max Prime 42/100, Claude Opus 5.5 39/100, GPT-6 Astra 36/100), so choose by what matters most for your work: Qwen 3.8 Max Prime 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 priceQwen 3.8 Max PrimeQwen 3.8 Max Prime $6.00 · Claude Opus 5.5 $8.00 · GPT-6 Astra $20.00 per 1M tokens (3:1 blend)
- Longest contextGPT-6 AstraGPT-6 Astra 1,050,000 · Claude Opus 5.5 1,000,000 · Qwen 3.8 Max Prime 1,000,000 tokens
- Widest inputsSame inputsGPT-6 Astra: Text, Images, PDFs · Claude Opus 5.5: Text, Images, PDFs · Qwen 3.8 Max Prime: Text, Images, Video
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | GPT-6 Astra | Claude Opus 5.5 | Qwen 3.8 Max Prime |
|---|---|---|---|---|
| Price | 50% | 0 | 7 | 13 |
| Inputs & features | 30% | 80 | 80 | 80 |
| Context window | 20% | 61 | 60 | 60 |
| Overall | 100% | 36/100 | 39/100 | 42/100 |
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 | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 166.5 | 167.4 (best) | — |
| ECI rank | #2 of 148 | #1 of 148 (best) | — |
| GPQA DiamondGraduate-level science questions | 95.8% (best) | 90.6% | — |
| FrontierMath Tiers 1–3Research-level mathematics | 93.7% (best) | 91.2% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 100% | 100% | — |
| SimpleQA VerifiedShort factual questions | 75.6% (best) | 72.2% | — |
| Price per million tokens | |||
| Input | $10.00 | $4.00 (best) | $4.00 (best) |
| Output | $50.00 | $20.00 | $12.00 (best) |
| Cached input | $1.00 | $0.20 (best) | — |
| Blended (3:1) | $20.00 | $8.00 | $6.00 (best) |
| Long-context rate | Over 272K: $20.00 / $75.00 | Same rate | Same rate |
| Price source | Official OpenAI API | Official Anthropic API | Median of 4 providers |
| Limits | |||
| Context window | 1,050,000 tokens (best) | 1,000,000 tokens | 1,000,000 tokens |
| Max output | 128,000 tokens | 128,000 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | Yes | Yes | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yeslow · medium · high · xhigh · max | Yeslow · medium · high · xhigh · max | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | gpt-6-astra | claude-opus-5-5 | — |
| API providers | 26 | 28 (best) | 4 |
| Released | Sep 4, 2026 | Sep 22, 2026 | Sep 23, 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
Claude Opus 5.5$80.00
Qwen 3.8 Max Prime$64.00
Which should you choose?
Which is better: GPT-6 Astra, Claude Opus 5.5 or Qwen 3.8 Max Prime?
It is close. Our weighted score puts them within 3 points (Qwen 3.8 Max Prime 42/100, Claude Opus 5.5 39/100, GPT-6 Astra 36/100), so choose by what matters most for your work: Qwen 3.8 Max Prime 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 Opus 5.5 or Qwen 3.8 Max Prime?
Qwen 3.8 Max Prime is cheaper at $4.00 input / $12.00 output per million tokens (median across 4 API providers). Claude Opus 5.5 costs $4.00 input / $20.00 output per million tokens (official Anthropic API price); GPT-6 Astra costs $10.00 input / $50.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $6.00 per million tokens for Qwen 3.8 Max Prime versus $8.00 for Claude Opus 5.5 (1.3× as much) and $20.00 for GPT-6 Astra (3.3× as much).
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 Opus 5.5 has an ECI of 167.4 and Qwen 3.8 Max Prime has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-6 Astra, Claude Opus 5.5 and Qwen 3.8 Max Prime 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 Opus 5.5 and 1,000,000 for Qwen 3.8 Max Prime. Maximum output per response: GPT-6 Astra up to 128,000, Claude Opus 5.5 up to 128,000, Qwen 3.8 Max Prime up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GPT-6 Astra accepts text, images and PDFs; Claude Opus 5.5 accepts text, images and PDFs; Qwen 3.8 Max Prime accepts text, images and video. They handle the same number of input types.
Are any of these open source?
No. GPT-6 Astra, Claude Opus 5.5 and Qwen 3.8 Max Prime are proprietary and only available through APIs and apps.
Which is newer?
Qwen 3.8 Max Prime is the newest, released Sep 23, 2026. Claude Opus 5.5 came out Sep 22, 2026; GPT-6 Astra came out Sep 4, 2026. Knowledge cutoff: GPT-6 Astra Apr 30, 2026, Claude Opus 5.5 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.