o3-pro vs Claude Opus 4.1 vs Pixtral Large (25.02)
Pixtral Large (25.02) comes out ahead, 33 to 27 and 27 on our weighted score, and it is the cheaper option too.
OpenAIo3-pro
27/100- ECI147.4
- Price$20.00 / $80.00
- Context200K
AnthropicClaude Opus 4.1
27/100- ECI144.1
- Price$15.00 / $75.00
- Context200K
- Our pick
Mistral AIPixtral Large (25.02)
33/100- ECI—
- Price$2.00 / $6.00
- Context128K
Pixtral Large (25.02) is our pick
Pixtral Large (25.02) is the better all-round choice, scoring 33/100 against o3-pro (27) and Claude Opus 4.1 (27). 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 pricePixtral Large (25.02)Pixtral Large (25.02) $3.00 · Claude Opus 4.1 $30.00 · o3-pro $35.00 per 1M tokens (3:1 blend)
- Longest contexto3-pro and Claude Opus 4.1o3-pro 200,000 · Claude Opus 4.1 200,000 · Pixtral Large (25.02) 128,000 tokens
- Widest inputsClaude Opus 4.1o3-pro: Text, Images · Claude Opus 4.1: Text, Images, PDFs · Pixtral Large (25.02): Text, Images
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | o3-pro | Claude Opus 4.1 | Pixtral Large (25.02) |
|---|---|---|---|---|
| Price | 50% | 0 | 0 | 27 |
| Inputs & features | 30% | 70 | 70 | 50 |
| Context window | 20% | 32 | 32 | 24 |
| Overall | 100% | 27/100 | 27/100 | 33/100 |
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 | o3-proOpenAI | Claude Opus 4.1Anthropic | Pixtral Large (25.02)Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 147.4 (best) | 144.1 | — |
| ECI rank | #60 of 148 (best) | #81 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 77.3% | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 12.6% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 68.9% | — |
| SWE-bench VerifiedFixing real GitHub issues | — | 73.4% | — |
| Price per million tokens | |||
| Input | $20.00 | $15.00 | $2.00 (best) |
| Output | $80.00 | $75.00 | $6.00 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $35.00 | $30.00 | $3.00 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Median of 14 providers | Median of 3 providers |
| Limits | |||
| Context window | 200,000 tokens (best) | 200,000 tokens (best) | 128,000 tokens |
| Max output | 100,000 tokens (best) | 32,000 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · medium · high | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | o3-pro | — | — |
| API providers | 6 | 14 (best) | 3 |
| Released | Jun 10, 2025 | Aug 5, 2025 | Apr 8, 2025 |
| Knowledge cutoff | May 2024 | Mar 31, 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- o3-pro$360.00
- Claude Opus 4.1$300.00
- Pixtral Large (25.02)$32.00
Which should you choose?
Which is better: o3-pro, Claude Opus 4.1 or Pixtral Large (25.02)?
Pixtral Large (25.02) is the better all-round choice, scoring 33/100 against o3-pro (27) and Claude Opus 4.1 (27). 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, o3-pro, Claude Opus 4.1 or Pixtral Large (25.02)?
Pixtral Large (25.02) is cheaper at $2.00 input / $6.00 output per million tokens (median across 3 API providers). Claude Opus 4.1 costs $15.00 input / $75.00 output per million tokens (median across 14 API providers); o3-pro costs $20.00 input / $80.00 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 Pixtral Large (25.02) versus $30.00 for Claude Opus 4.1 (10× as much) and $35.00 for o3-pro (12× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. o3-pro has an ECI of 147.4, Claude Opus 4.1 has an ECI of 144.1 and Pixtral Large (25.02) has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for o3-pro and Pixtral Large (25.02) 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?
o3-pro and Claude Opus 4.1 have the largest context windows (200,000 and 200,000 tokens), against 128,000 for Pixtral Large (25.02). Maximum output per response: o3-pro up to 100,000, Claude Opus 4.1 up to 32,000, Pixtral Large (25.02) up to 8,192 tokens.
Which can read images, PDFs, audio or video?
o3-pro accepts text and images; Claude Opus 4.1 accepts text, images and PDFs; Pixtral Large (25.02) accepts text and images. Claude Opus 4.1 handles the widest range of inputs.
Are any of these open source?
No. o3-pro, Claude Opus 4.1 and Pixtral Large (25.02) are proprietary and only available through APIs and apps.
Which is newer?
Claude Opus 4.1 is the newest, released Aug 5, 2025. o3-pro came out Jun 10, 2025; Pixtral Large (25.02) came out Apr 8, 2025. Knowledge cutoff: o3-pro May 2024, Claude Opus 4.1 Mar 31, 2025.
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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