Mistral Large 2.1 vs Mistral Large 2407 vs Claude Haiku 3.5
Claude Haiku 3.5 comes out ahead, 46 to 39 and 38 on our weighted score, and it is the cheaper option too.
Mistral AI
Mistral Large 2.1
38/100- ECI128.6
- Price$2.00 / $6.00
- Context131K
Mistral AI
Mistral Large 2407
39/100- ECI127.6
- Price$2.00 / $6.00
- Context131K
- Our pick
Anthropic
Claude Haiku 3.5
46/100- ECI127.0
- Price$0.80 / $4.00
- Context200K
Claude Haiku 3.5 is our pick
Claude Haiku 3.5 is the better all-round choice, scoring 46/100 against Mistral Large 2407 (39) and Mistral Large 2.1 (38). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMistral Large 2.1Capabilities Index (ECI): Mistral Large 2.1 128.6 · Mistral Large 2407 127.6 · Claude Haiku 3.5 127.0
- Lowest priceClaude Haiku 3.5Claude Haiku 3.5 $1.60 · Mistral Large 2.1 $3.00 · Mistral Large 2407 $3.00 per 1M tokens (3:1 blend)
- Longest contextClaude Haiku 3.5Claude Haiku 3.5 200,000 · Mistral Large 2.1 131,072 · Mistral Large 2407 131,072 tokens
- Widest inputsClaude Haiku 3.5Mistral Large 2.1: Text · Mistral Large 2407: Text, PDFs · Claude Haiku 3.5: Text, Images, PDFs
- Self-hostingMistral Large 2.1 and Mistral Large 2407Publishes downloadable weights
| Measure | Weight | Mistral Large 2.1 | Mistral Large 2407 | Claude Haiku 3.5 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 50 | 49 | 48 |
| Price | 25% | 26 | 26 | 38 |
| Inputs & features | 15% | 25 | 35 | 60 |
| Context window | 10% | 25 | 25 | 32 |
| Overall | 100% | 38/100 | 39/100 | 46/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 128.6 (best) | 127.6 | 127.0 |
| ECI rank | #142 of 165 (best) | #144 of 165 | #147 of 165 |
| GPQA DiamondGraduate-level science questions | 51.3% (best) | 49.0% | 38.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 7.8% | 8.5% (best) | 4.3% |
| Price per million tokens | |||
| Input | $2.00 | $2.00 | $0.80 (best) |
| Output | $6.00 | $6.00 | $4.00 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $3.00 | $3.00 | $1.60 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 5 providers | Median of 3 providers |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 200,000 tokens (best) |
| Max output | 16,384 tokens | 104,857 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | Yes | Yes |
| 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 | Open | Proprietary |
| API model ID | — | — | — |
| API providers | 2 | 5 (best) | 4 |
| Released | Nov 18, 2024 | Jul 24, 2024 | Oct 22, 2024 |
| Knowledge cutoff | Nov 2024 | Mar 31, 2024 | Jul 31, 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
Mistral Large 2.1$32.00
Mistral Large 2407$32.00
Claude Haiku 3.5$16.00
Which should you choose?
Which is better: Mistral Large 2.1, Mistral Large 2407 or Claude Haiku 3.5?
Claude Haiku 3.5 is the better all-round choice, scoring 46/100 against Mistral Large 2407 (39) and Mistral Large 2.1 (38). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Mistral Large 2.1, Mistral Large 2407 or Claude Haiku 3.5?
Claude Haiku 3.5 is cheaper at $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); Mistral Large 2407 costs $2.00 input / $6.00 output per million tokens (median across 5 API providers). At a typical mix of three input tokens to one output token, that is $1.60 per million tokens for Claude Haiku 3.5 versus $3.00 for Mistral Large 2.1 (1.9× as much) and $3.00 for Mistral Large 2407 (1.9× as much).
Which scores higher on benchmarks?
Mistral Large 2.1 scores higher on the Capabilities Index (ECI): Mistral Large 2.1 128.6 (#142 of 165), Mistral Large 2407 127.6 (#144 of 165) and Claude Haiku 3.5 127.0 (#147 of 165). The confidence ranges of the top two overlap (124.3–130.7 vs 123.3–129.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, Mistral Large 2407 49.0%, Claude Haiku 3.5 38.1%; OTIS Mock AIME 2024–2025 — Mistral Large 2407 8.5%, 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 Mistral Large 2.1, Mistral Large 2407 and Claude Haiku 3.5 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 Mistral Large 2407. Maximum output per response: Mistral Large 2.1 up to 16,384, Mistral Large 2407 up to 104,857, Claude Haiku 3.5 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Mistral Large 2.1 accepts text; Mistral Large 2407 accepts text and PDFs; Claude Haiku 3.5 accepts text, images and PDFs. Claude Haiku 3.5 handles the widest range of inputs.
Are any of these open source?
Mistral Large 2.1 and Mistral Large 2407 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; Mistral Large 2407 came out Jul 24, 2024. Knowledge cutoff: Mistral Large 2.1 Nov 2024, Mistral Large 2407 Mar 31, 2024, Claude Haiku 3.5 Jul 31, 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.