Qwen2.5 7B Instruct vs Claude Haiku 3 vs Mistral Nemo
Too close to call on our weighted score (Mistral Nemo 48, Claude Haiku 3 47, Qwen2.5 7B Instruct 44). The right pick depends on what you value most.
Alibaba (Qwen)Qwen2.5 7B Instruct
44/100- ECI118.5
- Price$0.175 / $0.70
- Context131K
AnthropicClaude Haiku 3
47/100- ECI118.4
- Price$0.25 / $1.25
- Context200K
Mistral AIMistral Nemo
48/100- ECI118.7
- Price$0.15 / $0.15
- Context128K
Too close to call
It is close. Our weighted score puts them within a point (Mistral Nemo 48/100, Claude Haiku 3 47/100, Qwen2.5 7B Instruct 44/100), so choose by what matters most for your work: Mistral Nemo for raw capability and Claude Haiku 3 for long inputs. 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 · Claude Haiku 3 118.4
- Lowest priceMistral NemoMistral Nemo $0.15 · Qwen2.5 7B Instruct $0.306 · Claude Haiku 3 $0.50 per 1M tokens (3:1 blend)
- Longest contextClaude Haiku 3Claude Haiku 3 200,000 · Qwen2.5 7B Instruct 131,072 · Mistral Nemo 128,000 tokens
- Widest inputsClaude Haiku 3Qwen2.5 7B Instruct: Text · Claude Haiku 3: Text, Images, PDFs · Mistral Nemo: Text
- Self-hostingQwen2.5 7B Instruct and Mistral NemoPublishes downloadable weights
| Measure | Weight | Qwen2.5 7B Instruct | Claude Haiku 3 | Mistral Nemo |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 38 | 38 | 39 |
| Price | 25% | 74 | 64 | 89 |
| Inputs & features | 15% | 25 | 60 | 25 |
| Context window | 10% | 24 | 32 | 24 |
| Overall | 100% | 44/100 | 47/100 | 48/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen2.5 7B InstructAlibaba (Qwen) | Claude Haiku 3Anthropic | Mistral NemoMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 118.5 | 118.4 | 118.7 (best) |
| ECI rank | #141 of 148 | #143 of 148 | #140 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 35.5% | 36.3% (best) | 29.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | 2.5% (best) | 1.8% | — |
| Price per million tokens | |||
| Input | $0.175 | $0.25 | $0.15 (best) |
| Output | $0.70 | $1.25 | $0.15 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.306 | $0.50 | $0.15 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 2 providers | Official Mistral API |
| Limits | |||
| Context window | 131,072 tokens | 200,000 tokens (best) | 128,000 tokens |
| Max output | 8,192 tokens | 4,096 tokens | 128,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | 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 | Proprietary | Open |
| API model ID | qwen2-5-7b-instruct | — | mistral-nemo |
| API providers | 1 | 2 | 5 (best) |
| Released | Sep 19, 2024 | Mar 13, 2024 | Jul 1, 2024 |
| Knowledge cutoff | Apr 2024 | Aug 31, 2023 | Jul 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen2.5 7B Instruct$3.15
- Claude Haiku 3$5.00
- Mistral Nemo$1.80
Which should you choose?
Which is better: Qwen2.5 7B Instruct, Claude Haiku 3 or Mistral Nemo?
It is close. Our weighted score puts them within a point (Mistral Nemo 48/100, Claude Haiku 3 47/100, Qwen2.5 7B Instruct 44/100), so choose by what matters most for your work: Mistral Nemo for raw capability and Claude Haiku 3 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen2.5 7B Instruct, Claude Haiku 3 or Mistral Nemo?
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); Claude Haiku 3 costs $0.25 input / $1.25 output per million tokens (median across 2 API providers). 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 $0.50 for Claude Haiku 3 (3.3× 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 Claude Haiku 3 118.4 (#143 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 — Claude Haiku 3 36.3%, Qwen2.5 7B Instruct 35.5%, Mistral Nemo 29.9%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen2.5 7B Instruct, Claude Haiku 3 and Mistral Nemo 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?
Claude Haiku 3 has the largest context window at 200,000 tokens, against 131,072 for Qwen2.5 7B Instruct and 128,000 for Mistral Nemo. Maximum output per response: Qwen2.5 7B Instruct up to 8,192, Claude Haiku 3 up to 4,096, Mistral Nemo up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5 7B Instruct accepts text; Claude Haiku 3 accepts text, images and PDFs; Mistral Nemo accepts text. Claude Haiku 3 handles the widest range of inputs.
Are any of these open source?
Qwen2.5 7B Instruct and Mistral Nemo publishes its weights and can be self-hosted; Claude Haiku 3 is proprietary.
Which is newer?
Qwen2.5 7B Instruct is the newest, released Sep 19, 2024. Mistral Nemo came out Jul 1, 2024; Claude Haiku 3 came out Mar 13, 2024. Knowledge cutoff: Qwen2.5 7B Instruct Apr 2024, Claude Haiku 3 Aug 31, 2023, Mistral Nemo Jul 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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