Claude Opus 5.5 vs Qwen3.8 27B vs GPT-6.1 Sol
GPT-6.1 Sol comes out ahead, 72 to 69 and 66 on our weighted score, though Qwen3.8 27B is 4.3× cheaper per token.
AnthropicClaude Opus 5.5
69/100- ECI168.4
- Price$4.00 / $20.00
- Context1M
Alibaba (Qwen)Qwen3.8 27B
66/100- ECI151.8
- Price$0.40 / $2.55
- Context262K
- Our pick
OpenAIGPT-6.1 Sol
72/100- ECI167.8
- Price$2.00 / $10.00
- Context1.05M
GPT-6.1 Sol is our pick
GPT-6.1 Sol is the better all-round choice, scoring 72/100 against Claude Opus 5.5 (69) and Qwen3.8 27B (66). Qwen3.8 27B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityClaude Opus 5.5Capabilities Index (ECI): Claude Opus 5.5 168.4 · GPT-6.1 Sol 167.8 · Qwen3.8 27B 151.8
- Lowest priceQwen3.8 27BQwen3.8 27B $0.938 · GPT-6.1 Sol $4.00 · Claude Opus 5.5 $8.00 per 1M tokens (3:1 blend)
- Longest contextGPT-6.1 SolGPT-6.1 Sol 1,050,000 · Claude Opus 5.5 1,000,000 · Qwen3.8 27B 262,144 tokens
- Widest inputsSame inputsClaude Opus 5.5: Text, Images, PDFs · Qwen3.8 27B: Text, Images, Video · GPT-6.1 Sol: Text, Images, PDFs
- Self-hostingQwen3.8 27BPublishes downloadable weights
| Measure | Weight | Claude Opus 5.5 | Qwen3.8 27B | GPT-6.1 Sol |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 99 | 79 | 99 |
| Price | 25% | 7 | 49 | 20 |
| Inputs & features | 15% | 80 | 70 | 80 |
| Context window | 10% | 60 | 37 | 61 |
| Overall | 100% | 69/100 | 66/100 | 72/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Claude Opus 5.5Anthropic | Qwen3.8 27BAlibaba (Qwen) | GPT-6.1 SolOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 168.4 (best) | 151.8 | 167.8 |
| ECI rank | #2 of 165 (best) | #54 of 165 | #4 of 165 |
| GPQA DiamondGraduate-level science questions | 90.6% | — | 95.4% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 91.2% | — | 93.7% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 100% | — | 100% |
| SimpleQA VerifiedShort factual questions | 72.2% | — | 73.9% (best) |
| Price per million tokens | |||
| Input | $4.00 | $0.40 (best) | $2.00 |
| Output | $20.00 | $2.55 (best) | $10.00 |
| Cached input | $0.20 | — | $0.10 (best) |
| Blended (3:1) | $8.00 | $0.938 (best) | $4.00 |
| Long-context rate | Same rate | Same rate | Over 272K: $4.00 / $15.00 |
| Price source | Official Anthropic API | Median of 49 providers | Official OpenAI API |
| Limits | |||
| Context window | 1,000,000 tokens | 262,144 tokens | 1,050,000 tokens (best) |
| 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 | claude-opus-5-5 | — | gpt-6.1-sol |
| API providers | 36 | 53 (best) | 33 |
| Released | Sep 22, 2026 | Aug 14, 2026 | Sep 29, 2026 |
| Knowledge cutoff | Jun 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 Opus 5.5$80.00
- Qwen3.8 27B$9.10
- GPT-6.1 Sol$40.00
Which should you choose?
Which is better: Claude Opus 5.5, Qwen3.8 27B or GPT-6.1 Sol?
GPT-6.1 Sol is the better all-round choice, scoring 72/100 against Claude Opus 5.5 (69) and Qwen3.8 27B (66). Qwen3.8 27B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Claude Opus 5.5, Qwen3.8 27B or GPT-6.1 Sol?
Qwen3.8 27B is cheaper at $0.40 input / $2.55 output per million tokens (median across 49 API providers). GPT-6.1 Sol costs $2.00 input / $10.00 output per million tokens (official OpenAI API price); Claude Opus 5.5 costs $4.00 input / $20.00 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 $4.00 for GPT-6.1 Sol (4.3× as much) and $8.00 for Claude Opus 5.5 (8.5× as much).
Which scores higher on benchmarks?
Claude Opus 5.5 scores higher on the Capabilities Index (ECI): Claude Opus 5.5 168.4 (#2 of 165), GPT-6.1 Sol 167.8 (#4 of 165) and Qwen3.8 27B 151.8 (#54 of 165). The confidence ranges of the top two overlap (165.4–173.0 vs 164.3–172.5), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Claude Opus 5.5, Qwen3.8 27B and GPT-6.1 Sol yet, so there is no like-for-like coding score. On overall capability, Claude Opus 5.5 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-6.1 Sol has the largest context window at 1,050,000 tokens, against 1,000,000 for Claude Opus 5.5 and 262,144 for Qwen3.8 27B. Maximum output per response: Claude Opus 5.5 up to 128,000, Qwen3.8 27B up to 32,768, GPT-6.1 Sol up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Claude Opus 5.5 accepts text, images and PDFs; Qwen3.8 27B accepts text, images and video; GPT-6.1 Sol 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; Claude Opus 5.5 and GPT-6.1 Sol is proprietary.
Which is newer?
GPT-6.1 Sol is the newest, released Sep 29, 2026. Claude Opus 5.5 came out Sep 22, 2026; Qwen3.8 27B came out Aug 14, 2026. Knowledge cutoff: Claude Opus 5.5 Jun 2026, GPT-6.1 Sol 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 Nov 2, 2026.
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