o3-pro vs Magistral Medium vs Claude Opus 4.1
Too close to call on our weighted score (Magistral Medium 30, o3-pro 27, Claude Opus 4.1 27). The right pick depends on what you value most.
OpenAIo3-pro
27/100- ECI147.4
- Price$20.00 / $80.00
- Context200K
Mistral AIMagistral Medium
30/100- ECI—
- Price$2.00 / $5.00
- Context128K
AnthropicClaude Opus 4.1
27/100- ECI144.1
- Price$15.00 / $75.00
- Context200K
Too close to call
It is close. Our weighted score puts them within 2 points (Magistral Medium 30/100, o3-pro 27/100, Claude Opus 4.1 27/100), so choose by what matters most for your work: Magistral Medium 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 priceMagistral MediumMagistral Medium $2.75 · 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 · Magistral Medium 128,000 tokens
- Widest inputsClaude Opus 4.1o3-pro: Text, Images · Magistral Medium: Text · Claude Opus 4.1: Text, Images, PDFs
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | o3-pro | Magistral Medium | Claude Opus 4.1 |
|---|---|---|---|---|
| Price | 50% | 0 | 29 | 0 |
| Inputs & features | 30% | 70 | 35 | 70 |
| Context window | 20% | 32 | 24 | 32 |
| Overall | 100% | 27/100 | 30/100 | 27/100 |
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 | Magistral MediumMistral AI | Claude Opus 4.1Anthropic |
|---|---|---|---|
| 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 | $2.00 (best) | $15.00 |
| Output | $80.00 | $5.00 (best) | $75.00 |
| Cached input | — | — | — |
| Blended (3:1) | $35.00 | $2.75 (best) | $30.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Official Mistral API | Median of 14 providers |
| Limits | |||
| Context window | 200,000 tokens (best) | 128,000 tokens | 200,000 tokens (best) |
| Max output | 100,000 tokens (best) | 16,384 tokens | 32,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · medium · high | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | o3-pro | magistral-medium-latest | — |
| API providers | 6 | 4 | 14 (best) |
| Released | Jun 10, 2025 | Mar 17, 2025 | Aug 5, 2025 |
| Knowledge cutoff | May 2024 | Jun 2025 | 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
- Magistral Medium$30.00
- Claude Opus 4.1$300.00
Which should you choose?
Which is better: o3-pro, Magistral Medium or Claude Opus 4.1?
It is close. Our weighted score puts them within 2 points (Magistral Medium 30/100, o3-pro 27/100, Claude Opus 4.1 27/100), so choose by what matters most for your work: Magistral Medium 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, Magistral Medium or Claude Opus 4.1?
Magistral Medium is cheaper at $2.00 input / $5.00 output per million tokens (official Mistral API price). 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 $2.75 per million tokens for Magistral Medium versus $30.00 for Claude Opus 4.1 (11× as much) and $35.00 for o3-pro (13× 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, Magistral Medium has not been scored yet and Claude Opus 4.1 has an ECI of 144.1.
Which is better for coding?
There are no published SWE-bench Verified results for o3-pro and Magistral Medium 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 Magistral Medium. Maximum output per response: o3-pro up to 100,000, Magistral Medium up to 16,384, Claude Opus 4.1 up to 32,000 tokens.
Which can read images, PDFs, audio or video?
o3-pro accepts text and images; Magistral Medium accepts text; Claude Opus 4.1 accepts text, images and PDFs. Claude Opus 4.1 handles the widest range of inputs.
Are any of these open source?
No. o3-pro, Magistral Medium and Claude Opus 4.1 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; Magistral Medium came out Mar 17, 2025. Knowledge cutoff: o3-pro May 2024, Magistral Medium Jun 2025, 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.
