Mistral Nemo vs Mixtral 8x7B vs Qwen2.5 7B Instruct
Mistral Nemo comes out ahead, 48 to 44 and 37 on our weighted score, and it is the cheaper option too.
- Our pick
Mistral AIMistral Nemo
48/100- ECI118.7
- Price$0.15 / $0.15
- Context128K
- Mistral AI
Mixtral 8x7B
37/100- ECI118.5
- Price$0.70 / $0.70
- Context32K
Alibaba (Qwen)Qwen2.5 7B Instruct
44/100- ECI118.5
- Price$0.175 / $0.70
- Context131K
Mistral Nemo is our pick
Mistral Nemo is the better all-round choice, scoring 48/100 against Qwen2.5 7B Instruct (44) and Mixtral 8x7B (37). It leads on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMistral NemoCapabilities Index (ECI): Mistral Nemo 118.7 · Qwen2.5 7B Instruct 118.5 · Mixtral 8x7B 118.5
- Lowest priceMistral NemoMistral Nemo $0.15 · Qwen2.5 7B Instruct $0.306 · Mixtral 8x7B $0.70 per 1M tokens (3:1 blend)
- Longest contextQwen2.5 7B InstructQwen2.5 7B Instruct 131,072 · Mistral Nemo 128,000 · Mixtral 8x7B 32,000 tokens
- Widest inputsSame inputsMistral Nemo: Text · Mixtral 8x7B: Text · Qwen2.5 7B Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Mistral Nemo | Mixtral 8x7B | Qwen2.5 7B Instruct |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 39 | 38 | 38 |
| Price | 25% | 89 | 57 | 74 |
| Inputs & features | 15% | 25 | 25 | 25 |
| Context window | 10% | 24 | 0 | 24 |
| Overall | 100% | 48/100 | 37/100 | 44/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Mistral NemoMistral AI | Mixtral 8x7BMistral AI | Qwen2.5 7B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 118.7 (best) | 118.5 | 118.5 |
| ECI rank | #140 of 148 (best) | #142 of 148 | #141 of 148 |
| GPQA DiamondGraduate-level science questions | 29.9% | 30.6% | 35.5% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 2.5% |
| Price per million tokens | |||
| Input | $0.15 (best) | $0.70 | $0.175 |
| Output | $0.15 (best) | $0.70 | $0.70 |
| Cached input | — | — | — |
| Blended (3:1) | $0.15 (best) | $0.70 | $0.306 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Official Mistral API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 32,000 tokens | 131,072 tokens (best) |
| Max output | 128,000 tokens (best) | 32,000 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | mistral-nemo | open-mixtral-8x7b | qwen2-5-7b-instruct |
| API providers | 5 (best) | 1 | 1 |
| Released | Jul 1, 2024 | Dec 11, 2023 | Sep 19, 2024 |
| Knowledge cutoff | Jul 2024 | Jan 2024 | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Mistral Nemo$1.80
- Mixtral 8x7B$8.40
- Qwen2.5 7B Instruct$3.15
Which should you choose?
Which is better: Mistral Nemo, Mixtral 8x7B or Qwen2.5 7B Instruct?
Mistral Nemo is the better all-round choice, scoring 48/100 against Qwen2.5 7B Instruct (44) and Mixtral 8x7B (37). It leads on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Mistral Nemo, Mixtral 8x7B or Qwen2.5 7B Instruct?
Mistral Nemo is cheaper at $0.15 input / $0.15 output per million tokens (official Mistral API price). Qwen2.5 7B Instruct costs $0.175 input / $0.70 output per million tokens (official Alibaba API price); Mixtral 8x7B costs $0.70 input / $0.70 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Mistral Nemo versus $0.306 for Qwen2.5 7B Instruct (2× as much) and $0.70 for Mixtral 8x7B (4.7× as much).
Which scores higher on benchmarks?
Mistral Nemo scores higher on the Capabilities Index (ECI): Mistral Nemo 118.7 (#140 of 148), Qwen2.5 7B Instruct 118.5 (#141 of 148) and Mixtral 8x7B 118.5 (#142 of 148). The confidence ranges of the top two overlap (111.3–121.5 vs 110.7–121.3), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen2.5 7B Instruct 35.5%, Mixtral 8x7B 30.6%, Mistral Nemo 29.9%.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral Nemo, Mixtral 8x7B and Qwen2.5 7B Instruct yet, so there is no like-for-like coding score. On overall capability, Mistral Nemo 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 7B Instruct has the largest context window at 131,072 tokens, against 128,000 for Mistral Nemo and 32,000 for Mixtral 8x7B. Maximum output per response: Mistral Nemo up to 128,000, Mixtral 8x7B up to 32,000, Qwen2.5 7B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Mistral Nemo accepts text; Mixtral 8x7B accepts text; Qwen2.5 7B Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Qwen2.5 7B Instruct is the newest, released Sep 19, 2024. Mistral Nemo came out Jul 1, 2024; Mixtral 8x7B came out Dec 11, 2023. Knowledge cutoff: Mistral Nemo Jul 2024, Mixtral 8x7B Jan 2024, Qwen2.5 7B Instruct Apr 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 Oct 4, 2026.
