Qwen2.5 72B Instruct vs Claude Haiku 3.5 vs Mistral Large 2.1
Claude Haiku 3.5 comes out ahead, 49 to 43 and 42 on our weighted score.
Alibaba (Qwen)Qwen2.5 72B Instruct
43/100- ECI129.0
- Price$1.40 / $5.60
- Context131K
- 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
Claude Haiku 3.5 is our pick
Claude Haiku 3.5 is the better all-round choice, scoring 49/100 against Qwen2.5 72B Instruct (43) and Mistral Large 2.1 (42). It leads on inputs & features and context window. The score weighs capability 67%, inputs & features 20%, context window 13%.
- CapabilityQwen2.5 72B InstructCapabilities Index (ECI): Qwen2.5 72B Instruct 129.0 · Mistral Large 2.1 128.5 · Claude Haiku 3.5 127.2
- Lowest priceQwen2.5 72B InstructQwen2.5 72B Instruct $2.45 · 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 · Qwen2.5 72B Instruct 131,072 · Mistral Large 2.1 131,072 tokens
- Widest inputsClaude Haiku 3.5Qwen2.5 72B Instruct: Text · Claude Haiku 3.5: Text, Images, PDFs · Mistral Large 2.1: Text
- Self-hostingQwen2.5 72B Instruct and Mistral Large 2.1Publishes downloadable weights
| Measure | Weight | Qwen2.5 72B Instruct | Claude Haiku 3.5 | Mistral Large 2.1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 67% | 52 | 49 | 51 |
| Inputs & features | 20% | 25 | 60 | 25 |
| Context window | 13% | 24 | 32 | 24 |
| Overall | 100% | 43/100 | 49/100 | 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 | Qwen2.5 72B InstructAlibaba (Qwen) | Claude Haiku 3.5Anthropic | Mistral Large 2.1Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 129.0 (best) | 127.2 | 128.5 |
| ECI rank | #128 of 148 (best) | #134 of 148 | #130 of 148 |
| GPQA DiamondGraduate-level science questions | 49.2% | 38.1% | 51.3% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 8.1% (best) | 4.3% | 7.8% |
| Price per million tokens | |||
| Input | $1.40 (best) | — | $2.00 |
| Output | $5.60 (best) | — | $6.00 |
| Cached input | — | — | — |
| Blended (3:1) | $2.45 (best) | — | $3.00 |
| Long-context rate | Same rate | — | Same rate |
| Price source | Official Alibaba API | — | Official Mistral API |
| Limits | |||
| Context window | 131,072 tokens | 200,000 tokens (best) | 131,072 tokens |
| Max output | 8,192 tokens | 8,192 tokens | 16,384 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 | qwen2-5-72b-instruct | — | mistral-large-2411 |
| API providers | 1 | — | 2 (best) |
| Released | Sep 19, 2024 | Oct 22, 2024 | Nov 18, 2024 |
| Knowledge cutoff | Apr 2024 | Jul 31, 2024 | Nov 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 72B Instruct$25.20
- Claude Haiku 3.5—
- Mistral Large 2.1$32.00
Which should you choose?
Which is better: Qwen2.5 72B Instruct, Claude Haiku 3.5 or Mistral Large 2.1?
Claude Haiku 3.5 is the better all-round choice, scoring 49/100 against Qwen2.5 72B Instruct (43) and Mistral Large 2.1 (42). It leads on inputs & features and context window. The score weighs capability 67%, inputs & features 20%, context window 13%.
Which is cheaper, Qwen2.5 72B Instruct, Claude Haiku 3.5 or Mistral Large 2.1?
Qwen2.5 72B Instruct is cheaper at $1.40 input / $5.60 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 $2.45 per million tokens for Qwen2.5 72B Instruct versus $3.00 for Mistral Large 2.1 (1.2× as much). Claude Haiku 3.5 has no published per-token price.
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), Mistral Large 2.1 128.5 (#130 of 148) and Claude Haiku 3.5 127.2 (#134 of 148). The confidence ranges of the top two overlap (123.8–130.7 vs 123.8–130.8), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, Qwen2.5 72B Instruct 49.2%, Claude Haiku 3.5 38.1%; OTIS Mock AIME 2024–2025 — Qwen2.5 72B Instruct 8.1%, Mistral Large 2.1 7.8%, Claude Haiku 3.5 4.3%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen2.5 72B Instruct, Claude Haiku 3.5 and Mistral Large 2.1 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?
Claude Haiku 3.5 has the largest context window at 200,000 tokens, against 131,072 for Qwen2.5 72B Instruct and 131,072 for Mistral Large 2.1. Maximum output per response: Qwen2.5 72B Instruct up to 8,192, Claude Haiku 3.5 up to 8,192, Mistral Large 2.1 up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Qwen2.5 72B Instruct accepts text; Claude Haiku 3.5 accepts text, images and PDFs; Mistral Large 2.1 accepts text. Claude Haiku 3.5 handles the widest range of inputs.
Are any of these open source?
Qwen2.5 72B Instruct and Mistral Large 2.1 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 72B Instruct came out Sep 19, 2024. Knowledge cutoff: Qwen2.5 72B Instruct Apr 2024, Claude Haiku 3.5 Jul 31, 2024, Mistral Large 2.1 Nov 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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