Mixtral 8x22B vs Qwen2.5 72B Instruct vs GPT-4o
GPT-4o comes out ahead, 43 to 40 and 33 on our weighted score, though Qwen2.5 72B Instruct is 44% cheaper per token.
Mistral AI
Mixtral 8x22B
33/100- ECI122.0
- Price$2.00 / $6.00
- Context64K
Alibaba (Qwen)
Qwen2.5 72B Instruct
40/100- ECI129.0
- Price$1.40 / $5.60
- Context131K
- Our pick
OpenAI
GPT-4o
43/100- ECI129.0
- Price$2.50 / $10.00
- Context128K
GPT-4o is our pick
GPT-4o is the better all-round choice, scoring 43/100 against Qwen2.5 72B Instruct (40) and Mixtral 8x22B (33). It leads on inputs & features. Qwen2.5 72B Instruct wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen2.5 72B InstructCapabilities Index (ECI): Qwen2.5 72B Instruct 129.0 · GPT-4o 129.0 · Mixtral 8x22B 122.0
- Lowest priceQwen2.5 72B InstructQwen2.5 72B Instruct $2.45 · Mixtral 8x22B $3.00 · GPT-4o $4.38 per 1M tokens (3:1 blend)
- Longest contextQwen2.5 72B InstructQwen2.5 72B Instruct 131,072 · GPT-4o 128,000 · Mixtral 8x22B 64,000 tokens
- Widest inputsGPT-4oMixtral 8x22B: Text · Qwen2.5 72B Instruct: Text · GPT-4o: Text, Images, PDFs
- Self-hostingMixtral 8x22B and Qwen2.5 72B InstructPublishes downloadable weights
| Measure | Weight | Mixtral 8x22B | Qwen2.5 72B Instruct | GPT-4o |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 43 | 52 | 52 |
| Price | 25% | 27 | 31 | 19 |
| Inputs & features | 15% | 25 | 25 | 70 |
| Context window | 10% | 12 | 24 | 24 |
| Overall | 100% | 33/100 | 40/100 | 43/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 122.0 | 129.0 (best) | 129.0 |
| ECI rank | #139 of 148 | #128 of 148 (best) | #129 of 148 |
| GPQA DiamondGraduate-level science questions | 34.1% | 49.2% (best) | 48.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 8.1% (best) | 6.3% |
| Price per million tokens | |||
| Input | $2.00 | $1.40 (best) | $2.50 |
| Output | $6.00 | $5.60 (best) | $10.00 |
| Cached input | — | — | $1.25 |
| Blended (3:1) | $3.00 | $2.45 (best) | $4.38 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Official Alibaba API | Official OpenAI API |
| Limits | |||
| Context window | 64,000 tokens | 131,072 tokens (best) | 128,000 tokens |
| Max output | 64,000 tokens (best) | 8,192 tokens | 16,384 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | open-mixtral-8x22b | qwen2-5-72b-instruct | gpt-4o |
| API providers | 1 | 1 | 19 (best) |
| Released | Apr 17, 2024 | Sep 19, 2024 | May 13, 2024 |
| Knowledge cutoff | Apr 2024 | Apr 2024 | Sep 2023 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
Mixtral 8x22B$32.00
Qwen2.5 72B Instruct$25.20
GPT-4o$45.00
Which should you choose?
Which is better: Mixtral 8x22B, Qwen2.5 72B Instruct or GPT-4o?
GPT-4o is the better all-round choice, scoring 43/100 against Qwen2.5 72B Instruct (40) and Mixtral 8x22B (33). It leads on inputs & features. Qwen2.5 72B Instruct wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Mixtral 8x22B, Qwen2.5 72B Instruct or GPT-4o?
Qwen2.5 72B Instruct is cheaper at $1.40 input / $5.60 output per million tokens (official Alibaba API price). Mixtral 8x22B costs $2.00 input / $6.00 output per million tokens (official Mistral API price); 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 $2.45 per million tokens for Qwen2.5 72B Instruct versus $3.00 for Mixtral 8x22B (1.2× as much) and $4.38 for GPT-4o (1.8× as much).
Which scores higher on benchmarks?
Qwen2.5 72B Instruct scores higher on the Capabilities Index (ECI): Qwen2.5 72B Instruct 129.0 (#128 of 148), GPT-4o 129.0 (#129 of 148) and Mixtral 8x22B 122.0 (#139 of 148). The confidence ranges of the top two overlap (123.8–130.7 vs 124.2–131.5), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen2.5 72B Instruct 49.2%, GPT-4o 48.9%, Mixtral 8x22B 34.1%.
Which is better for coding?
There are no published SWE-bench Verified results for Mixtral 8x22B, Qwen2.5 72B Instruct and GPT-4o yet, so there is no like-for-like coding score. On overall capability, Qwen2.5 72B Instruct 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?
Qwen2.5 72B Instruct has the largest context window at 131,072 tokens, against 128,000 for GPT-4o and 64,000 for Mixtral 8x22B. Maximum output per response: Mixtral 8x22B up to 64,000, Qwen2.5 72B Instruct up to 8,192, GPT-4o up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Mixtral 8x22B accepts text; Qwen2.5 72B Instruct accepts text; GPT-4o accepts text, images and PDFs. GPT-4o handles the widest range of inputs.
Are any of these open source?
Mixtral 8x22B and Qwen2.5 72B Instruct publishes its weights and can be self-hosted; GPT-4o is proprietary.
Which is newer?
Qwen2.5 72B Instruct is the newest, released Sep 19, 2024. GPT-4o came out May 13, 2024; Mixtral 8x22B came out Apr 17, 2024. Knowledge cutoff: Mixtral 8x22B Apr 2024, Qwen2.5 72B Instruct Apr 2024, GPT-4o Sep 2023.
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.