GPT-4o vs Mistral Large 2407 vs Mixtral 8x22B Instruct
GPT-4o comes out ahead, 43 to 39 and 34 on our weighted score, though Mistral Large 2407 is 31% cheaper per token.
- Our pick
OpenAI
GPT-4o
43/100- ECI129.0
- Price$2.50 / $10.00
- Context128K
Mistral AI
Mistral Large 2407
39/100- ECI127.6
- Price$2.00 / $6.00
- Context131K
Mistral AI
Mixtral 8x22B Instruct
34/100- ECI122.0
- Price$2.00 / $6.00
- Context66K
GPT-4o is our pick
GPT-4o is the better all-round choice, scoring 43/100 against Mistral Large 2407 (39) and Mixtral 8x22B Instruct (34). It leads on capability and 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 2407 127.6 · Mixtral 8x22B Instruct 122.0
- Lowest priceMistral Large 2407 and Mixtral 8x22B InstructMistral Large 2407 $3.00 · Mixtral 8x22B Instruct $3.00 · GPT-4o $4.38 per 1M tokens (3:1 blend)
- Longest contextMistral Large 2407Mistral Large 2407 131,072 · GPT-4o 128,000 · Mixtral 8x22B Instruct 65,536 tokens
- Widest inputsGPT-4oGPT-4o: Text, Images, PDFs · Mistral Large 2407: Text, PDFs · Mixtral 8x22B Instruct: Text, PDFs
- Self-hostingMistral Large 2407 and Mixtral 8x22B InstructPublishes downloadable weights
| Measure | Weight | GPT-4o | Mistral Large 2407 | Mixtral 8x22B Instruct |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 51 | 49 | 42 |
| Price | 25% | 19 | 26 | 26 |
| Inputs & features | 15% | 70 | 35 | 35 |
| Context window | 10% | 24 | 25 | 12 |
| Overall | 100% | 43/100 | 39/100 | 34/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 129.0 (best) | 127.6 | 122.0 |
| ECI rank | #141 of 165 (best) | #144 of 165 | #155 of 165 |
| GPQA DiamondGraduate-level science questions | 48.9% | 49.0% (best) | 34.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 6.3% | 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 5 providers | Median of 5 providers |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 65,536 tokens |
| Max output | 16,384 tokens | 104,857 tokens (best) | 52,428 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | Yes | Yes | 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) | 5 | 5 |
| Released | May 13, 2024 | Jul 24, 2024 | Apr 17, 2024 |
| Knowledge cutoff | Sep 2023 | Mar 31, 2024 | Jan 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 2407$32.00
Mixtral 8x22B Instruct$32.00
Which should you choose?
Which is better: GPT-4o, Mistral Large 2407 or Mixtral 8x22B Instruct?
GPT-4o is the better all-round choice, scoring 43/100 against Mistral Large 2407 (39) and Mixtral 8x22B Instruct (34). It leads on capability and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT-4o, Mistral Large 2407 or Mixtral 8x22B Instruct?
Mistral Large 2407 is cheaper at $2.00 input / $6.00 output per million tokens (median across 5 API providers). Mixtral 8x22B Instruct 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 2407 versus $3.00 for Mixtral 8x22B Instruct (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 2407 127.6 (#144 of 165) and Mixtral 8x22B Instruct 122.0 (#155 of 165). The confidence ranges of the top two overlap (124.7–131.3 vs 123.3–129.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mistral Large 2407 49.0%, GPT-4o 48.9%, Mixtral 8x22B Instruct 34.1%.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-4o, Mistral Large 2407 and Mixtral 8x22B Instruct 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 2407 has the largest context window at 131,072 tokens, against 128,000 for GPT-4o and 65,536 for Mixtral 8x22B Instruct. Maximum output per response: GPT-4o up to 16,384, Mistral Large 2407 up to 104,857, Mixtral 8x22B Instruct up to 52,428 tokens.
Which can read images, PDFs, audio or video?
GPT-4o accepts text, images and PDFs; Mistral Large 2407 accepts text and PDFs; Mixtral 8x22B Instruct accepts text and PDFs. GPT-4o handles the widest range of inputs.
Are any of these open source?
Mistral Large 2407 and Mixtral 8x22B Instruct publishes its weights and can be self-hosted; GPT-4o is proprietary.
Which is newer?
Mistral Large 2407 is the newest, released Jul 24, 2024. GPT-4o came out May 13, 2024; Mixtral 8x22B Instruct came out Apr 17, 2024. Knowledge cutoff: GPT-4o Sep 2023, Mistral Large 2407 Mar 31, 2024, Mixtral 8x22B Instruct Jan 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.