Claude Haiku 3.5 vs Qwen3.8 27B vs Mistral Large 2.1
Qwen3.8 27B comes out ahead, 66 to 46 and 38 on our weighted score, and it is the cheaper option too.
AnthropicClaude Haiku 3.5
46/100- ECI127.0
- Price$0.80 / $4.00
- Context200K
- Our pick
Alibaba (Qwen)Qwen3.8 27B
66/100- ECI151.8
- Price$0.40 / $2.55
- Context262K
Mistral AIMistral Large 2.1
38/100- ECI128.6
- Price$2.00 / $6.00
- Context131K
Qwen3.8 27B is our pick
Qwen3.8 27B is the better all-round choice, scoring 66/100 against Claude Haiku 3.5 (46) and Mistral Large 2.1 (38). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.8 27BCapabilities Index (ECI): Qwen3.8 27B 151.8 · Mistral Large 2.1 128.6 · Claude Haiku 3.5 127.0
- Lowest priceQwen3.8 27BQwen3.8 27B $0.938 · Claude Haiku 3.5 $1.60 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
- Longest contextQwen3.8 27BQwen3.8 27B 262,144 · Claude Haiku 3.5 200,000 · Mistral Large 2.1 131,072 tokens
- Widest inputsClaude Haiku 3.5 and Qwen3.8 27BClaude Haiku 3.5: Text, Images, PDFs · Qwen3.8 27B: Text, Images, Video · Mistral Large 2.1: Text
- Self-hostingQwen3.8 27B and Mistral Large 2.1Publishes downloadable weights
| Measure | Weight | Claude Haiku 3.5 | Qwen3.8 27B | Mistral Large 2.1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 48 | 79 | 50 |
| Price | 25% | 38 | 49 | 26 |
| Inputs & features | 15% | 60 | 70 | 25 |
| Context window | 10% | 32 | 37 | 25 |
| Overall | 100% | 46/100 | 66/100 | 38/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Claude Haiku 3.5Anthropic | Qwen3.8 27BAlibaba (Qwen) | Mistral Large 2.1Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 127.0 | 151.8 (best) | 128.6 |
| ECI rank | #147 of 165 | #54 of 165 (best) | #142 of 165 |
| GPQA DiamondGraduate-level science questions | 38.1% | — | 51.3% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 4.3% | — | 7.8% (best) |
| Price per million tokens | |||
| Input | $0.80 | $0.40 (best) | $2.00 |
| Output | $4.00 | $2.55 (best) | $6.00 |
| Cached input | — | — | — |
| Blended (3:1) | $1.60 | $0.938 (best) | $3.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 49 providers | Median of 2 providers |
| Limits | |||
| Context window | 200,000 tokens | 262,144 tokens (best) | 131,072 tokens |
| Max output | 8,192 tokens | 32,768 tokens (best) | 16,384 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | Yes | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | — | — | — |
| API providers | 4 | 53 (best) | 2 |
| Released | Oct 22, 2024 | Aug 14, 2026 | Nov 18, 2024 |
| Knowledge cutoff | 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.
- Claude Haiku 3.5$16.00
- Qwen3.8 27B$9.10
- Mistral Large 2.1$32.00
Which should you choose?
Which is better: Claude Haiku 3.5, Qwen3.8 27B or Mistral Large 2.1?
Qwen3.8 27B is the better all-round choice, scoring 66/100 against Claude Haiku 3.5 (46) and Mistral Large 2.1 (38). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Claude Haiku 3.5, Qwen3.8 27B or Mistral Large 2.1?
Qwen3.8 27B is cheaper at $0.40 input / $2.55 output per million tokens (median across 49 API providers). Claude Haiku 3.5 costs $0.80 input / $4.00 output per million tokens (median across 3 API providers); Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.938 per million tokens for Qwen3.8 27B versus $1.60 for Claude Haiku 3.5 (1.7× as much) and $3.00 for Mistral Large 2.1 (3.2× as much).
Which scores higher on benchmarks?
Qwen3.8 27B scores higher on the Capabilities Index (ECI): Qwen3.8 27B 151.8 (#54 of 165), Mistral Large 2.1 128.6 (#142 of 165) and Claude Haiku 3.5 127.0 (#147 of 165). Their confidence ranges do not overlap (149.9–153.6 vs 124.3–130.7), so the gap is a real one.
Which is better for coding?
There are no published SWE-bench Verified results for Claude Haiku 3.5, Qwen3.8 27B and Mistral Large 2.1 yet, so there is no like-for-like coding score. On overall capability, Qwen3.8 27B 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?
Qwen3.8 27B has the largest context window at 262,144 tokens, against 200,000 for Claude Haiku 3.5 and 131,072 for Mistral Large 2.1. Maximum output per response: Claude Haiku 3.5 up to 8,192, Qwen3.8 27B up to 32,768, Mistral Large 2.1 up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Claude Haiku 3.5 accepts text, images and PDFs; Qwen3.8 27B accepts text, images and video; Mistral Large 2.1 accepts text. Claude Haiku 3.5 handles the widest range of inputs.
Are any of these open source?
Qwen3.8 27B and Mistral Large 2.1 publishes its weights and can be self-hosted; Claude Haiku 3.5 is proprietary.
Which is newer?
Qwen3.8 27B is the newest, released Aug 14, 2026. Mistral Large 2.1 came out Nov 18, 2024; Claude Haiku 3.5 came out Oct 22, 2024. Knowledge cutoff: 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 Nov 2, 2026.
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