Claude Haiku 3.5 vs Mistral Large 2.1 vs Mistral Large 4
Mistral Large 4 comes out ahead, 67 to 46 and 38 on our weighted score, and it is the cheaper option too.
Anthropic
Claude Haiku 3.5
46/100- ECI127.0
- Price$0.80 / $4.00
- Context200K
Mistral AI
Mistral Large 2.1
38/100- ECI128.6
- Price$2.00 / $6.00
- Context131K
- Our pick
Mistral AI
Mistral Large 4
67/100- ECI150.6
- Price$0.70 / $2.10
- Context1.05M
Mistral Large 4 is our pick
Mistral Large 4 is the better all-round choice, scoring 67/100 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%.
- CapabilityMistral Large 4Capabilities Index (ECI): Mistral Large 4 150.6 · Mistral Large 2.1 128.6 · Claude Haiku 3.5 127.0
- Lowest priceMistral Large 4Mistral Large 4 $1.05 · Claude Haiku 3.5 $1.60 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
- Longest contextMistral Large 4Mistral Large 4 1,048,576 · Claude Haiku 3.5 200,000 · Mistral Large 2.1 131,072 tokens
- Widest inputsClaude Haiku 3.5Claude Haiku 3.5: Text, Images, PDFs · Mistral Large 2.1: Text · Mistral Large 4: Text, Images
- Self-hostingMistral Large 2.1Publishes downloadable weights
| Measure | Weight | Claude Haiku 3.5 | Mistral Large 2.1 | Mistral Large 4 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 48 | 50 | 77 |
| Price | 25% | 38 | 26 | 47 |
| Inputs & features | 15% | 60 | 25 | 70 |
| Context window | 10% | 32 | 25 | 61 |
| Overall | 100% | 46/100 | 38/100 | 67/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 127.0 | 128.6 | 150.6 (best) |
| ECI rank | #147 of 165 | #142 of 165 | #57 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 38.1% | 51.3% | 88.1% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 58.6% |
| OTIS Mock AIME 2024–2025Competition mathematics | 4.3% | 7.8% | 98.3% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 20.0% |
| Price per million tokens | |||
| Input | $0.80 | $2.00 | $0.70 (best) |
| Output | $4.00 | $6.00 | $2.10 (best) |
| Cached input | — | — | $0.07 |
| Blended (3:1) | $1.60 | $3.00 | $1.05 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 3 providers | Median of 2 providers | Official Mistral API |
| Limits | |||
| Context window | 200,000 tokens | 131,072 tokens | 1,048,576 tokens (best) |
| Max output | 8,192 tokens | 16,384 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | Yes | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | Yeslow · medium · high |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | — | — | mistral-large-4 |
| API providers | 4 | 2 | 15 (best) |
| Released | Oct 22, 2024 | Nov 18, 2024 | Oct 6, 2026 |
| 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
Mistral Large 2.1$32.00
Mistral Large 4$11.20
Which should you choose?
Which is better: Claude Haiku 3.5, Mistral Large 2.1 or Mistral Large 4?
Mistral Large 4 is the better all-round choice, scoring 67/100 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, Mistral Large 2.1 or Mistral Large 4?
Mistral Large 4 is cheaper at $0.70 input / $2.10 output per million tokens (official Mistral API price). 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 $1.05 per million tokens for Mistral Large 4 versus $1.60 for Claude Haiku 3.5 (1.5× as much) and $3.00 for Mistral Large 2.1 (2.9× as much).
Which scores higher on benchmarks?
Mistral Large 4 scores higher on the Capabilities Index (ECI): Mistral Large 4 150.6 (#57 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 (147.4–153.5 vs 124.3–130.7), so the gap is a real one. On individual benchmarks: GPQA Diamond — Mistral Large 4 88.1%, Mistral Large 2.1 51.3%, Claude Haiku 3.5 38.1%; OTIS Mock AIME 2024–2025 — Mistral Large 4 98.3%, 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 Claude Haiku 3.5, Mistral Large 2.1 and Mistral Large 4 yet, so there is no like-for-like coding score. On overall capability, Mistral Large 4 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?
Mistral Large 4 has the largest context window at 1,048,576 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, Mistral Large 2.1 up to 16,384, Mistral Large 4 up to 262,144 tokens.
Which can read images, PDFs, audio or video?
Claude Haiku 3.5 accepts text, images and PDFs; Mistral Large 2.1 accepts text; Mistral Large 4 accepts text and images. Claude Haiku 3.5 handles the widest range of inputs.
Are any of these open source?
Mistral Large 2.1 publishes its weights and can be self-hosted; Claude Haiku 3.5 and Mistral Large 4 is proprietary.
Which is newer?
Mistral Large 4 is the newest, released Oct 6, 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.