GPT-4o vs Mistral Large 2.1 vs Mistral Large 2407
GPT-4o comes out ahead, 43 to 39 and 38 on our weighted score, though Mistral Large 2.1 is 31% cheaper per token.
- Our pick
OpenAIGPT-4o
43/100- ECI129.0
- Price$2.50 / $10.00
- Context128K
Mistral AIMistral Large 2.1
38/100- ECI128.6
- Price$2.00 / $6.00
- Context131K
- Mistral AI
Mistral Large 2407
39/100- ECI127.6
- Price$2.00 / $6.00
- Context131K
GPT-4o is our pick
GPT-4o is the better all-round choice, scoring 43/100 against Mistral Large 2407 (39) and Mistral Large 2.1 (38). It leads on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT-4oCapabilities Index (ECI): GPT-4o 129.0 · Mistral Large 2.1 128.6 · Mistral Large 2407 127.6
- Lowest priceMistral Large 2.1 and Mistral Large 2407Mistral Large 2.1 $3.00 · Mistral Large 2407 $3.00 · GPT-4o $4.38 per 1M tokens (3:1 blend)
- Longest contextMistral Large 2.1 and Mistral Large 2407Mistral Large 2.1 131,072 · Mistral Large 2407 131,072 · GPT-4o 128,000 tokens
- Widest inputsGPT-4oGPT-4o: Text, Images, PDFs · Mistral Large 2.1: Text · Mistral Large 2407: Text, PDFs
- Self-hostingMistral Large 2.1 and Mistral Large 2407Publishes downloadable weights
| Measure | Weight | GPT-4o | Mistral Large 2.1 | Mistral Large 2407 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 51 | 50 | 49 |
| Price | 25% | 19 | 26 | 26 |
| Inputs & features | 15% | 70 | 25 | 35 |
| Context window | 10% | 24 | 25 | 25 |
| Overall | 100% | 43/100 | 38/100 | 39/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-4oOpenAI | Mistral Large 2.1Mistral AI | Mistral Large 2407Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 129.0 (best) | 128.6 | 127.6 |
| ECI rank | #141 of 165 (best) | #142 of 165 | #144 of 165 |
| GPQA DiamondGraduate-level science questions | 48.9% | 51.3% (best) | 49.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | 6.3% | 7.8% | 8.5% (best) |
| Price per million tokens | |||
| Input | $2.50 | $2.00 (best) | $2.00 (best) |
| Output | $10.00 | $6.00 (best) | $6.00 (best) |
| Cached input | $1.25 | — | — |
| Blended (3:1) | $4.38 | $3.00 (best) | $3.00 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Median of 2 providers | Median of 5 providers |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Max output | 16,384 tokens | 16,384 tokens | 104,857 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | Yes | No | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | gpt-4o | — | — |
| API providers | 26 (best) | 2 | 5 |
| Released | May 13, 2024 | Nov 18, 2024 | Jul 24, 2024 |
| Knowledge cutoff | Sep 2023 | Nov 2024 | Mar 31, 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-4o$45.00
- Mistral Large 2.1$32.00
- Mistral Large 2407$32.00
Which should you choose?
Which is better: GPT-4o, Mistral Large 2.1 or Mistral Large 2407?
GPT-4o is the better all-round choice, scoring 43/100 against Mistral Large 2407 (39) and Mistral Large 2.1 (38). It leads on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-4o, Mistral Large 2.1 or Mistral Large 2407?
Mistral Large 2.1 is cheaper at $2.00 input / $6.00 output per million tokens (median across 2 API providers). Mistral Large 2407 costs $2.00 input / $6.00 output per million tokens (median across 5 API providers); GPT-4o costs $2.50 input / $10.00 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 Mistral Large 2.1 versus $3.00 for Mistral Large 2407 (1× as much) and $4.38 for GPT-4o (1.5× as much).
Which scores higher on benchmarks?
GPT-4o scores higher on the Capabilities Index (ECI): GPT-4o 129.0 (#141 of 165), Mistral Large 2.1 128.6 (#142 of 165) and Mistral Large 2407 127.6 (#144 of 165). The confidence ranges of the top two overlap (124.7–131.3 vs 124.3–130.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, Mistral Large 2407 49.0%, GPT-4o 48.9%; OTIS Mock AIME 2024–2025 — Mistral Large 2407 8.5%, Mistral Large 2.1 7.8%, GPT-4o 6.3%.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-4o, Mistral Large 2.1 and Mistral Large 2407 yet, so there is no like-for-like coding score. On overall capability, GPT-4o 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?
Mistral Large 2.1 and Mistral Large 2407 have the largest context windows (131,072 and 131,072 tokens), against 128,000 for GPT-4o. Maximum output per response: GPT-4o up to 16,384, Mistral Large 2.1 up to 16,384, Mistral Large 2407 up to 104,857 tokens.
Which can read images, PDFs, audio or video?
GPT-4o accepts text, images and PDFs; Mistral Large 2.1 accepts text; Mistral Large 2407 accepts text and PDFs. GPT-4o handles the widest range of inputs.
Are any of these open source?
Mistral Large 2.1 and Mistral Large 2407 publishes its weights and can be self-hosted; GPT-4o is proprietary.
Which is newer?
Mistral Large 2.1 is the newest, released Nov 18, 2024. Mistral Large 2407 came out Jul 24, 2024; GPT-4o came out May 13, 2024. Knowledge cutoff: GPT-4o Sep 2023, Mistral Large 2.1 Nov 2024, Mistral Large 2407 Mar 31, 2024.
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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