GPT-5.5 vs Claude Opus 4.8 vs Qwen3.8 Max Preview
Qwen3.8 Max Preview comes out ahead, 47 to 37 and 36 on our weighted score, and it is the cheaper option too.
OpenAIGPT-5.5
36/100- ECI159.2
- Price$5.00 / $30.00
- Context1.05M
AnthropicClaude Opus 4.8
37/100- ECI158.3
- Price$5.00 / $25.00
- Context1M
- Our pick
Alibaba (Qwen)Qwen3.8 Max Preview
47/100- ECI—
- Price$2.00 / $6.00
- Context1M
Qwen3.8 Max Preview is our pick
Qwen3.8 Max Preview is the better all-round choice, scoring 47/100 against Claude Opus 4.8 (37) and GPT-5.5 (36). It leads on price. 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 priceQwen3.8 Max PreviewQwen3.8 Max Preview $3.00 · Claude Opus 4.8 $10.00 · GPT-5.5 $11.25 per 1M tokens (3:1 blend)
- Longest contextGPT-5.5GPT-5.5 1,050,000 · Claude Opus 4.8 1,000,000 · Qwen3.8 Max Preview 1,000,000 tokens
- Widest inputsSame inputsGPT-5.5: Text, Images, PDFs · Claude Opus 4.8: Text, Images, PDFs · Qwen3.8 Max Preview: Text, Images, Video
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | GPT-5.5 | Claude Opus 4.8 | Qwen3.8 Max Preview |
|---|---|---|---|---|
| Price | 50% | 0 | 2 | 27 |
| Inputs & features | 30% | 80 | 80 | 70 |
| Context window | 20% | 61 | 60 | 60 |
| Overall | 100% | 36/100 | 37/100 | 47/100 |
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-5.5OpenAI | Claude Opus 4.8Anthropic | Qwen3.8 Max PreviewAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 159.2 (best) | 158.3 | — |
| ECI rank | #10 of 148 (best) | #12 of 148 | — |
| GPQA DiamondGraduate-level science questions | 94.0% (best) | 91.0% | — |
| FrontierMath Tiers 1–3Research-level mathematics | 85.3% (best) | 80.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 100% (best) | 98.3% | — |
| SWE-bench VerifiedFixing real GitHub issues | 80.6% | — | — |
| SimpleQA VerifiedShort factual questions | 63.0% (best) | 53.0% | — |
| Price per million tokens | |||
| Input | $5.00 | $5.00 | $2.00 (best) |
| Output | $30.00 | $25.00 | $6.00 (best) |
| Cached input | $0.50 | $0.50 | — |
| Blended (3:1) | $11.25 | $10.00 | $3.00 (best) |
| Long-context rate | Over 272K: $10.00 / $45.00 | Same rate | Same rate |
| Price source | Official OpenAI API | Official Anthropic API | Median of 6 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 | Yeslow · medium · high · xhigh · max | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | gpt-5.5 | claude-opus-4-8 | — |
| API providers | 42 | 45 (best) | 6 |
| Released | Apr 23, 2026 | May 28, 2026 | Jul 19, 2026 |
| Knowledge cutoff | Dec 1, 2025 | Jan 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-5.5$110.00
- Claude Opus 4.8$100.00
- Qwen3.8 Max Preview$32.00
Which should you choose?
Which is better: GPT-5.5, Claude Opus 4.8 or Qwen3.8 Max Preview?
Qwen3.8 Max Preview is the better all-round choice, scoring 47/100 against Claude Opus 4.8 (37) and GPT-5.5 (36). It leads on price. 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-5.5, Claude Opus 4.8 or Qwen3.8 Max Preview?
Qwen3.8 Max Preview is cheaper at $2.00 input / $6.00 output per million tokens (median across 6 API providers). Claude Opus 4.8 costs $5.00 input / $25.00 output per million tokens (official Anthropic API price); GPT-5.5 costs $5.00 input / $30.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $3.00 per million tokens for Qwen3.8 Max Preview versus $10.00 for Claude Opus 4.8 (3.3× as much) and $11.25 for GPT-5.5 (3.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5.5 has an ECI of 159.2, Claude Opus 4.8 has an ECI of 158.3 and Qwen3.8 Max Preview has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Claude Opus 4.8 and Qwen3.8 Max Preview 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-5.5 has the largest context window at 1,050,000 tokens, against 1,000,000 for Claude Opus 4.8 and 1,000,000 for Qwen3.8 Max Preview. Maximum output per response: GPT-5.5 up to 128,000, Claude Opus 4.8 up to 128,000, Qwen3.8 Max Preview up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GPT-5.5 accepts text, images and PDFs; Claude Opus 4.8 accepts text, images and PDFs; Qwen3.8 Max Preview accepts text, images and video. They handle the same number of input types.
Are any of these open source?
No. GPT-5.5, Claude Opus 4.8 and Qwen3.8 Max Preview are proprietary and only available through APIs and apps.
Which is newer?
Qwen3.8 Max Preview is the newest, released Jul 19, 2026. Claude Opus 4.8 came out May 28, 2026; GPT-5.5 came out Apr 23, 2026. Knowledge cutoff: GPT-5.5 Dec 1, 2025, Claude Opus 4.8 Jan 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.
