Claude Haiku 3.5 vs Mistral Large 2.1 vs Qwen2.5 32B Instruct
Claude Haiku 3.5 comes out ahead, 49 to 42 and 42 on our weighted score.
- Our pick
AnthropicClaude Haiku 3.5
49/100- ECI127.2
- Price—
- Context200K
Mistral AIMistral Large 2.1
42/100- ECI128.5
- Price$2.00 / $6.00
- Context131K
Alibaba (Qwen)Qwen2.5 32B Instruct
42/100- ECI128.5
- Price$0.70 / $2.80
- Context131K
Claude Haiku 3.5 is our pick
Claude Haiku 3.5 is the better all-round choice, scoring 49/100 against Mistral Large 2.1 (42) and Qwen2.5 32B Instruct (42). It leads on inputs & features and context window. The score weighs capability 67%, inputs & features 20%, context window 13%.
- CapabilityMistral Large 2.1 and Qwen2.5 32B InstructCapabilities Index (ECI): Mistral Large 2.1 128.5 · Qwen2.5 32B Instruct 128.5 · Claude Haiku 3.5 127.2
- Lowest priceQwen2.5 32B InstructQwen2.5 32B Instruct $1.23 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend) · Claude Haiku 3.5 unpriced
- Longest contextClaude Haiku 3.5Claude Haiku 3.5 200,000 · Mistral Large 2.1 131,072 · Qwen2.5 32B Instruct 131,072 tokens
- Widest inputsClaude Haiku 3.5Claude Haiku 3.5: Text, Images, PDFs · Mistral Large 2.1: Text · Qwen2.5 32B Instruct: Text
- Self-hostingMistral Large 2.1 and Qwen2.5 32B InstructPublishes downloadable weights
| Measure | Weight | Claude Haiku 3.5 | Mistral Large 2.1 | Qwen2.5 32B Instruct |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 67% | 49 | 51 | 51 |
| Inputs & features | 20% | 60 | 25 | 25 |
| Context window | 13% | 32 | 24 | 24 |
| Overall | 100% | 49/100 | 42/100 | 42/100 |
Left out because at least one model lacks the data: price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Claude Haiku 3.5Anthropic | Mistral Large 2.1Mistral AI | Qwen2.5 32B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 127.2 | 128.5 (best) | 128.5 (best) |
| ECI rank | #134 of 148 | #130 of 148 (best) | #131 of 148 |
| GPQA DiamondGraduate-level science questions | 38.1% | 51.3% (best) | 46.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 4.3% | 7.8% (best) | 7.4% |
| Price per million tokens | |||
| Input | — | $2.00 | $0.70 (best) |
| Output | — | $6.00 | $2.80 (best) |
| Cached input | — | — | — |
| Blended (3:1) | — | $3.00 | $1.23 (best) |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Official Mistral API | Official Alibaba API |
| Limits | |||
| Context window | 200,000 tokens (best) | 131,072 tokens | 131,072 tokens |
| Max output | 8,192 tokens | 16,384 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | Yes | 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 | Proprietary | Open | Open |
| API model ID | — | mistral-large-2411 | qwen2-5-32b-instruct |
| API providers | — | 2 (best) | 1 |
| Released | Oct 22, 2024 | Nov 18, 2024 | Sep 17, 2024 |
| Knowledge cutoff | Jul 31, 2024 | Nov 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.
- Claude Haiku 3.5—
- Mistral Large 2.1$32.00
- Qwen2.5 32B Instruct$12.60
Which should you choose?
Which is better: Claude Haiku 3.5, Mistral Large 2.1 or Qwen2.5 32B Instruct?
Claude Haiku 3.5 is the better all-round choice, scoring 49/100 against Mistral Large 2.1 (42) and Qwen2.5 32B Instruct (42). It leads on inputs & features and context window. The score weighs capability 67%, inputs & features 20%, context window 13%.
Which is cheaper, Claude Haiku 3.5, Mistral Large 2.1 or Qwen2.5 32B Instruct?
Qwen2.5 32B Instruct is cheaper at $0.70 input / $2.80 output per million tokens (official Alibaba API price). Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $1.23 per million tokens for Qwen2.5 32B Instruct versus $3.00 for Mistral Large 2.1 (2.4× as much). Claude Haiku 3.5 has no published per-token price.
Which scores higher on benchmarks?
Mistral Large 2.1 scores higher on the Capabilities Index (ECI): Mistral Large 2.1 128.5 (#130 of 148), Qwen2.5 32B Instruct 128.5 (#131 of 148) and Claude Haiku 3.5 127.2 (#134 of 148). The confidence ranges of the top two overlap (123.8–130.8 vs 123.5–130.0), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, Qwen2.5 32B Instruct 46.1%, Claude Haiku 3.5 38.1%; OTIS Mock AIME 2024–2025 — Mistral Large 2.1 7.8%, Qwen2.5 32B Instruct 7.4%, Claude Haiku 3.5 4.3%.
Which is better for coding?
There are no published SWE-bench Verified results for Claude Haiku 3.5, Mistral Large 2.1 and Qwen2.5 32B Instruct yet, so there is no like-for-like coding score. On overall capability, Mistral Large 2.1 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 3.5 has the largest context window at 200,000 tokens, against 131,072 for Mistral Large 2.1 and 131,072 for Qwen2.5 32B Instruct. Maximum output per response: Claude Haiku 3.5 up to 8,192, Mistral Large 2.1 up to 16,384, Qwen2.5 32B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Claude Haiku 3.5 accepts text, images and PDFs; Mistral Large 2.1 accepts text; Qwen2.5 32B Instruct accepts text. Claude Haiku 3.5 handles the widest range of inputs.
Are any of these open source?
Mistral Large 2.1 and Qwen2.5 32B Instruct publishes its weights and can be self-hosted; Claude Haiku 3.5 is proprietary.
Which is newer?
Mistral Large 2.1 is the newest, released Nov 18, 2024. Claude Haiku 3.5 came out Oct 22, 2024; Qwen2.5 32B Instruct came out Sep 17, 2024. Knowledge cutoff: Claude Haiku 3.5 Jul 31, 2024, Mistral Large 2.1 Nov 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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