Mistral Large 2.1 vs Llama-3.2-1B vs Claude Haiku 3.5
Too close to call on our weighted score (Llama-3.2-1B 34, Claude Haiku 3.5 32, Mistral Large 2.1 27). The right pick depends on what you value most.
Mistral AIMistral Large 2.1
27/100- ECI128.6
- Price$2.00 / $6.00
- Context131K
MetaLlama-3.2-1B
34/100- ECI—
- Price$0.027 / $0.201
- Context131K
AnthropicClaude Haiku 3.5
32/100- ECI127.0
- Price$0.80 / $4.00
- Context200K
Too close to call
It is close. Our weighted score puts them within 1 points (Llama-3.2-1B 34/100, Claude Haiku 3.5 32/100, Mistral Large 2.1 27/100), so choose by what matters most for your work: Mistral Large 2.1 for raw capability, Llama-3.2-1B on price and Claude Haiku 3.5 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because Llama-3.2-1B has no Capabilities Index score yet.
- CapabilityMistral Large 2.1Shared benchmarks: Mistral Large 2.1 29.6% · Claude Haiku 3.5 21.2% · Llama-3.2-1B 12.2%
- Lowest priceLlama-3.2-1BLlama-3.2-1B $0.07 · Claude Haiku 3.5 $1.60 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
- Longest contextClaude Haiku 3.5Claude Haiku 3.5 200,000 · Mistral Large 2.1 131,072 · Llama-3.2-1B 131,072 tokens
- Widest inputsClaude Haiku 3.5Mistral Large 2.1: Text · Llama-3.2-1B: Text · Claude Haiku 3.5: Text, Images, PDFs
- Self-hostingMistral Large 2.1 and Llama-3.2-1BPublishes downloadable weights (Llama 3.2 Community License)
| Measure | Weight | Mistral Large 2.1 | Llama-3.2-1B | Claude Haiku 3.5 |
|---|---|---|---|---|
| CapabilityShared benchmarks | 50% | 30 | 12 | 21 |
| Price | 25% | 26 | 100 | 38 |
| Inputs & features | 15% | 25 | 0 | 60 |
| Context window | 10% | 25 | 25 | 32 |
| Overall | 100% | 27/100 | 34/100 | 32/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Mistral Large 2.1Mistral AI | Llama-3.2-1BMeta | Claude Haiku 3.5Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 128.6 (best) | — | 127.0 |
| ECI rank | #142 of 165 (best) | — | #147 of 165 |
| GPQA DiamondGraduate-level science questions | 51.3% (best) | 23.9% | 38.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 7.8% (best) | 0.6% | 4.3% |
| Price per million tokens | |||
| Input | $2.00 | $0.027 (best) | $0.80 |
| Output | $6.00 | $0.201 (best) | $4.00 |
| Cached input | — | — | — |
| Blended (3:1) | $3.00 | $0.07 (best) | $1.60 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 6 providers | Median of 3 providers |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 200,000 tokens (best) |
| Max output | 16,384 tokens (best) | 8,192 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenLlama 3.2 Community License | Proprietary |
| API model ID | — | — | — |
| API providers | 2 | 6 (best) | 4 |
| Released | Nov 18, 2024 | Sep 25, 2024 | Oct 22, 2024 |
| Knowledge cutoff | Nov 2024 | Dec 2023 | 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
- Llama-3.2-1B$0.672
- Claude Haiku 3.5$16.00
Which should you choose?
Which is better: Mistral Large 2.1, Llama-3.2-1B or Claude Haiku 3.5?
It is close. Our weighted score puts them within 1 points (Llama-3.2-1B 34/100, Claude Haiku 3.5 32/100, Mistral Large 2.1 27/100), so choose by what matters most for your work: Mistral Large 2.1 for raw capability, Llama-3.2-1B on price and Claude Haiku 3.5 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because Llama-3.2-1B has no Capabilities Index score yet.
Which is cheaper, Mistral Large 2.1, Llama-3.2-1B or Claude Haiku 3.5?
Llama-3.2-1B is cheaper at $0.027 input / $0.201 output per million tokens (median across 6 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 $0.07 per million tokens for Llama-3.2-1B versus $1.60 for Claude Haiku 3.5 (23× as much) and $3.00 for Mistral Large 2.1 (43× as much).
Which scores higher on benchmarks?
Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025): Mistral Large 2.1 29.6%, Claude Haiku 3.5 21.2% and Llama-3.2-1B 12.2%. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, Claude Haiku 3.5 38.1%, Llama-3.2-1B 23.9%; OTIS Mock AIME 2024–2025 — Mistral Large 2.1 7.8%, Claude Haiku 3.5 4.3%, Llama-3.2-1B 0.6%.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral Large 2.1, Llama-3.2-1B 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. Note that Llama-3.2-1B does not support tool calling, which most coding agents need.
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 Llama-3.2-1B. Maximum output per response: Mistral Large 2.1 up to 16,384, Llama-3.2-1B up to 8,192, Claude Haiku 3.5 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Mistral Large 2.1 accepts text; Llama-3.2-1B accepts text; 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 Llama-3.2-1B publishes its weights (Llama 3.2 Community License) 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; Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Mistral Large 2.1 Nov 2024, Llama-3.2-1B Dec 2023, 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.
