Gemini 3.1 Pro Preview vs GPT-5.6 Sol
Too close to call on our weighted score (Gemini 3.1 Pro Preview 68, GPT-5.6 Sol 66). The right pick depends on what you value most.
GoogleGemini 3.1 Pro Preview
68/100- ECI154.8
- Price$2.00 / $12.00
- Context1.05M
OpenAIGPT-5.6 Sol
66/100- ECI161.8
- Price$4.00 / $20.00
- Context1.05M
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Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within 2 points (Gemini 3.1 Pro Preview 68/100, GPT-5.6 Sol 66/100), so choose by what matters most for your work: GPT-5.6 Sol for raw capability and Gemini 3.1 Pro Preview on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT-5.6 SolCapabilities Index (ECI): GPT-5.6 Sol 161.8 · Gemini 3.1 Pro Preview 154.8
- Lowest priceGemini 3.1 Pro PreviewGemini 3.1 Pro Preview $4.50 · GPT-5.6 Sol $8.00 per 1M tokens (3:1 blend)
- Longest contextAbout the sameGPT-5.6 Sol 1,050,000 · Gemini 3.1 Pro Preview 1,048,576 tokens
- Widest inputsGemini 3.1 Pro PreviewGemini 3.1 Pro Preview: Text, Images, PDFs, Audio, Video · GPT-5.6 Sol: Text, Images, PDFs
- Self-hostingNo open weightsBoth are available only through APIs
| Measure | Weight | Gemini 3.1 Pro Preview | GPT-5.6 Sol |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 84 | 93 |
| Price | 25% | 19 | 7 |
| Inputs & features | 15% | 100 | 80 |
| Context window | 10% | 61 | 61 |
| Overall | 100% | 68/100 | 66/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemini 3.1 Pro PreviewGoogle | GPT-5.6 SolOpenAI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 154.8 | 161.8 (best) |
| ECI rank | #31 of 148 | #8 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 94.4% (best) | 93.5% |
| FrontierMath Tiers 1–3Research-level mathematics | 59.7% | 89.1% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 95.6% | 100% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 75.6% | — |
| SimpleQA VerifiedShort factual questions | 73.5% (best) | 69.7% |
| Price per million tokens | ||
| Input | $2.00 (best) | $4.00 |
| Output | $12.00 (best) | $20.00 |
| Cached input | $0.20 (best) | $0.40 |
| Blended (3:1) | $4.50 (best) | $8.00 |
| Long-context rate | Over 200K: $4.00 / $18.00 | Over 272K: $8.00 / $30.00 |
| Price source | Official Google API | Official OpenAI API |
| Limits | ||
| Context window | 1,048,576 tokens | 1,050,000 tokens (best) |
| Max output | 65,536 tokens | 128,000 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | Yes | Yes |
| Audio | Yes | No |
| Video | Yes | No |
| Reasoning | Yeslow · medium · high | Yeslow · medium · high · xhigh · max |
| Tool calling | Yes | Yes |
| Structured output | Yes | Yes |
| Availability | ||
| Weights | Proprietary | Proprietary |
| API model ID | gemini-3.1-pro-preview | gpt-5.6-sol |
| API providers | 26 | 40 (best) |
| Released | Feb 19, 2026 | Jul 9, 2026 |
| Knowledge cutoff | Jan 2025 | Feb 16, 2026 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Gemini 3.1 Pro Preview$44.00
- GPT-5.6 Sol$80.00
Which should you choose?
Which is better: Gemini 3.1 Pro Preview or GPT-5.6 Sol?
It is close. Our weighted score puts them within 2 points (Gemini 3.1 Pro Preview 68/100, GPT-5.6 Sol 66/100), so choose by what matters most for your work: GPT-5.6 Sol for raw capability and Gemini 3.1 Pro Preview on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Gemini 3.1 Pro Preview or GPT-5.6 Sol?
Gemini 3.1 Pro Preview is cheaper at $2.00 input / $12.00 output per million tokens (official Google API price). GPT-5.6 Sol costs $4.00 input / $20.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $4.50 per million tokens for Gemini 3.1 Pro Preview versus $8.00 for GPT-5.6 Sol (1.8× as much).
Which scores higher on benchmarks?
GPT-5.6 Sol scores higher on the Capabilities Index (ECI): GPT-5.6 Sol 161.8 (#8 of 148) and Gemini 3.1 Pro Preview 154.8 (#31 of 148). Their confidence ranges do not overlap (159.3–165.3 vs 152.6–157.3), so the gap is a real one. On individual benchmarks: GPQA Diamond — Gemini 3.1 Pro Preview 94.4%, GPT-5.6 Sol 93.5%; FrontierMath Tiers 1–3 — GPT-5.6 Sol 89.1%, Gemini 3.1 Pro Preview 59.7%; OTIS Mock AIME 2024–2025 — GPT-5.6 Sol 100%, Gemini 3.1 Pro Preview 95.6%; SimpleQA Verified — Gemini 3.1 Pro Preview 73.5%, GPT-5.6 Sol 69.7%.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.6 Sol yet, so there is no like-for-like coding score. On overall capability, GPT-5.6 Sol leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.
Which has the bigger context window?
Their context windows are effectively the same size: Gemini 3.1 Pro Preview 1,048,576 and GPT-5.6 Sol 1,050,000 tokens. Maximum output per response: Gemini 3.1 Pro Preview up to 65,536, GPT-5.6 Sol up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Gemini 3.1 Pro Preview accepts text, images, PDFs, audio and video; GPT-5.6 Sol accepts text, images and PDFs. Gemini 3.1 Pro Preview handles the widest range of inputs.
Are any of these open source?
No. Gemini 3.1 Pro Preview and GPT-5.6 Sol are proprietary and only available through APIs and apps.
Which is newer?
GPT-5.6 Sol is the newest, released Jul 9, 2026. Gemini 3.1 Pro Preview came out Feb 19, 2026. Knowledge cutoff: Gemini 3.1 Pro Preview Jan 2025, GPT-5.6 Sol Feb 16, 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.
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