Mixtral 8x7B vs Nova Pro vs Qwen2.5 32B Instruct
Nova Pro comes out ahead, 48 to 43 and 37 on our weighted score, though Mixtral 8x7B is 2× cheaper per token.
Mistral AIMixtral 8x7B
37/100- ECI118.5
- Price$0.70 / $0.70
- Context32K
- Our pick
AmazonNova Pro
48/100- ECI123.8
- Price$0.80 / $3.20
- Context300K
Alibaba (Qwen)Qwen2.5 32B Instruct
43/100- ECI128.5
- Price$0.70 / $2.80
- Context131K
Nova Pro is our pick
Nova Pro is the better all-round choice, scoring 48/100 against Qwen2.5 32B Instruct (43) and Mixtral 8x7B (37). It leads on inputs & features and context window. Mixtral 8x7B wins on price. Qwen2.5 32B Instruct wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen2.5 32B InstructCapabilities Index (ECI): Qwen2.5 32B Instruct 128.5 · Nova Pro 123.8 · Mixtral 8x7B 118.5
- Lowest priceMixtral 8x7BMixtral 8x7B $0.70 · Qwen2.5 32B Instruct $1.23 · Nova Pro $1.40 per 1M tokens (3:1 blend)
- Longest contextNova ProNova Pro 300,000 · Qwen2.5 32B Instruct 131,072 · Mixtral 8x7B 32,000 tokens
- Widest inputsNova ProMixtral 8x7B: Text · Nova Pro: Text, Images, PDFs, Video · Qwen2.5 32B Instruct: Text
- Self-hostingMixtral 8x7B and Qwen2.5 32B InstructPublishes downloadable weights
| Measure | Weight | Mixtral 8x7B | Nova Pro | Qwen2.5 32B Instruct |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 38 | 45 | 51 |
| Price | 25% | 57 | 43 | 46 |
| Inputs & features | 15% | 25 | 70 | 25 |
| Context window | 10% | 0 | 39 | 24 |
| Overall | 100% | 37/100 | 48/100 | 43/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Mixtral 8x7BMistral AI | Nova ProAmazon | Qwen2.5 32B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 118.5 | 123.8 | 128.5 (best) |
| ECI rank | #142 of 148 | #137 of 148 | #131 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 30.6% | — | 46.1% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 7.4% |
| Price per million tokens | |||
| Input | $0.70 (best) | $0.80 | $0.70 (best) |
| Output | $0.70 (best) | $3.20 | $2.80 |
| Cached input | — | $0.20 | — |
| Blended (3:1) | $0.70 (best) | $1.40 | $1.23 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Official Amazon Bedrock API | Official Alibaba API |
| Limits | |||
| Context window | 32,000 tokens | 300,000 tokens (best) | 131,072 tokens |
| Max output | 32,000 tokens (best) | 10,000 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | open-mixtral-8x7b | amazon.nova-pro-v1:0 | qwen2-5-32b-instruct |
| API providers | 1 | 3 (best) | 1 |
| Released | Dec 11, 2023 | Dec 3, 2024 | Sep 17, 2024 |
| Knowledge cutoff | Jan 2024 | Oct 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.
- Mixtral 8x7B$8.40
- Nova Pro$14.40
- Qwen2.5 32B Instruct$12.60
Which should you choose?
Which is better: Mixtral 8x7B, Nova Pro or Qwen2.5 32B Instruct?
Nova Pro is the better all-round choice, scoring 48/100 against Qwen2.5 32B Instruct (43) and Mixtral 8x7B (37). It leads on inputs & features and context window. Mixtral 8x7B wins on price. Qwen2.5 32B Instruct wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Mixtral 8x7B, Nova Pro or Qwen2.5 32B Instruct?
Mixtral 8x7B is cheaper at $0.70 input / $0.70 output per million tokens (official Mistral API price). Qwen2.5 32B Instruct costs $0.70 input / $2.80 output per million tokens (official Alibaba API price); Nova Pro costs $0.80 input / $3.20 output per million tokens (official Amazon Bedrock API price). At a typical mix of three input tokens to one output token, that is $0.70 per million tokens for Mixtral 8x7B versus $1.23 for Qwen2.5 32B Instruct (1.8× as much) and $1.40 for Nova Pro (2× as much).
Which scores higher on benchmarks?
Qwen2.5 32B Instruct scores higher on the Capabilities Index (ECI): Qwen2.5 32B Instruct 128.5 (#131 of 148), Nova Pro 123.8 (#137 of 148) and Mixtral 8x7B 118.5 (#142 of 148). The confidence ranges of the top two overlap (123.5–130.0 vs 108.8–126.6), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Mixtral 8x7B, Nova Pro and Qwen2.5 32B Instruct yet, so there is no like-for-like coding score. On overall capability, Qwen2.5 32B 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?
Nova Pro has the largest context window at 300,000 tokens, against 131,072 for Qwen2.5 32B Instruct and 32,000 for Mixtral 8x7B. Maximum output per response: Mixtral 8x7B up to 32,000, Nova Pro up to 10,000, Qwen2.5 32B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Mixtral 8x7B accepts text; Nova Pro accepts text, images, PDFs and video; Qwen2.5 32B Instruct accepts text. Nova Pro handles the widest range of inputs.
Are any of these open source?
Mixtral 8x7B and Qwen2.5 32B Instruct publishes its weights and can be self-hosted; Nova Pro is proprietary.
Which is newer?
Nova Pro is the newest, released Dec 3, 2024. Qwen2.5 32B Instruct came out Sep 17, 2024; Mixtral 8x7B came out Dec 11, 2023. Knowledge cutoff: Mixtral 8x7B Jan 2024, Nova Pro Oct 2024, Qwen2.5 32B 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.
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