Mistral Nemo vs Claude Haiku 3 vs Llama-3.1-8B-Instruct
Too close to call on our weighted score (Mistral Nemo 48, Claude Haiku 3 47, Llama-3.1-8B-Instruct 46). The right pick depends on what you value most.
Mistral AIMistral Nemo
48/100- ECI118.7
- Price$0.15 / $0.15
- Context128K
AnthropicClaude Haiku 3
47/100- ECI118.4
- Price$0.25 / $1.25
- Context200K
MetaLlama-3.1-8B-Instruct
46/100- ECI116.6
- Price$0.152 / $0.167
- Context128K
Too close to call
It is close. Our weighted score puts them within a point (Mistral Nemo 48/100, Claude Haiku 3 47/100, Llama-3.1-8B-Instruct 46/100), so choose by what matters most for your work: Mistral Nemo for raw capability and Claude Haiku 3 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMistral NemoCapabilities Index (ECI): Mistral Nemo 118.7 · Claude Haiku 3 118.4 · Llama-3.1-8B-Instruct 116.6
- Lowest priceMistral NemoMistral Nemo $0.15 · Llama-3.1-8B-Instruct $0.156 · Claude Haiku 3 $0.50 per 1M tokens (3:1 blend)
- Longest contextClaude Haiku 3Claude Haiku 3 200,000 · Mistral Nemo 128,000 · Llama-3.1-8B-Instruct 128,000 tokens
- Widest inputsClaude Haiku 3Mistral Nemo: Text · Claude Haiku 3: Text, Images, PDFs · Llama-3.1-8B-Instruct: Text
- Self-hostingMistral Nemo and Llama-3.1-8B-InstructPublishes downloadable weights
| Measure | Weight | Mistral Nemo | Claude Haiku 3 | Llama-3.1-8B-Instruct |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 39 | 38 | 36 |
| Price | 25% | 89 | 64 | 88 |
| Inputs & features | 15% | 25 | 60 | 25 |
| Context window | 10% | 24 | 32 | 24 |
| Overall | 100% | 48/100 | 47/100 | 46/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Mistral NemoMistral AI | Claude Haiku 3Anthropic | Llama-3.1-8B-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 118.7 (best) | 118.4 | 116.6 |
| ECI rank | #140 of 148 (best) | #143 of 148 | #145 of 148 |
| GPQA DiamondGraduate-level science questions | 29.9% | 36.3% (best) | 27.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 1.8% (best) | 1.7% |
| Price per million tokens | |||
| Input | $0.15 (best) | $0.25 | $0.152 |
| Output | $0.15 (best) | $1.25 | $0.167 |
| Cached input | — | — | — |
| Blended (3:1) | $0.15 (best) | $0.50 | $0.156 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Mistral API | Median of 2 providers | Median of 9 providers |
| Limits | |||
| Context window | 128,000 tokens | 200,000 tokens (best) | 128,000 tokens |
| Max output | 128,000 tokens (best) | 4,096 tokens | 4,096 tokens |
| 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 | mistral-nemo | — | — |
| API providers | 5 | 2 | 9 (best) |
| Released | Jul 1, 2024 | Mar 13, 2024 | Jul 23, 2024 |
| Knowledge cutoff | Jul 2024 | Aug 31, 2023 | Dec 2023 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Mistral Nemo$1.80
- Claude Haiku 3$5.00
- Llama-3.1-8B-Instruct$1.85
Which should you choose?
Which is better: Mistral Nemo, Claude Haiku 3 or Llama-3.1-8B-Instruct?
It is close. Our weighted score puts them within a point (Mistral Nemo 48/100, Claude Haiku 3 47/100, Llama-3.1-8B-Instruct 46/100), so choose by what matters most for your work: Mistral Nemo for raw capability and Claude Haiku 3 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Mistral Nemo, Claude Haiku 3 or Llama-3.1-8B-Instruct?
Mistral Nemo is cheaper at $0.15 input / $0.15 output per million tokens (official Mistral API price). Llama-3.1-8B-Instruct costs $0.152 input / $0.167 output per million tokens (median across 9 API providers); Claude Haiku 3 costs $0.25 input / $1.25 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Mistral Nemo versus $0.156 for Llama-3.1-8B-Instruct (1× as much) and $0.50 for Claude Haiku 3 (3.3× as much).
Which scores higher on benchmarks?
Mistral Nemo scores higher on the Capabilities Index (ECI): Mistral Nemo 118.7 (#140 of 148), Claude Haiku 3 118.4 (#143 of 148) and Llama-3.1-8B-Instruct 116.6 (#145 of 148). The confidence ranges of the top two overlap (111.3–121.5 vs 110.5–121.2), so treat the gap as small. On individual benchmarks: GPQA Diamond — Claude Haiku 3 36.3%, Mistral Nemo 29.9%, Llama-3.1-8B-Instruct 27.0%.
Which is better for coding?
There are no published SWE-bench Verified results for Mistral Nemo, Claude Haiku 3 and Llama-3.1-8B-Instruct yet, so there is no like-for-like coding score. On overall capability, Mistral Nemo 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 has the largest context window at 200,000 tokens, against 128,000 for Mistral Nemo and 128,000 for Llama-3.1-8B-Instruct. Maximum output per response: Mistral Nemo up to 128,000, Claude Haiku 3 up to 4,096, Llama-3.1-8B-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Mistral Nemo accepts text; Claude Haiku 3 accepts text, images and PDFs; Llama-3.1-8B-Instruct accepts text. Claude Haiku 3 handles the widest range of inputs.
Are any of these open source?
Mistral Nemo and Llama-3.1-8B-Instruct publishes its weights and can be self-hosted; Claude Haiku 3 is proprietary.
Which is newer?
Llama-3.1-8B-Instruct is the newest, released Jul 23, 2024. Mistral Nemo came out Jul 1, 2024; Claude Haiku 3 came out Mar 13, 2024. Knowledge cutoff: Mistral Nemo Jul 2024, Claude Haiku 3 Aug 31, 2023, Llama-3.1-8B-Instruct Dec 2023.
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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