Llama-3.1-8B-Instruct vs Mistral Large 2.1 vs Mistral Nemo
Too close to call on our weighted score (Mistral Nemo 48, Llama-3.1-8B-Instruct 46, Mistral Large 2.1 38). The right pick depends on what you value most.
MetaLlama-3.1-8B-Instruct
46/100- ECI116.6
- Price$0.152 / $0.167
- Context128K
Mistral AIMistral Large 2.1
38/100- ECI128.5
- Price$2.00 / $6.00
- Context131K
- Mistral AI
Mistral Nemo
48/100- ECI118.7
- Price$0.15 / $0.15
- Context128K
Too close to call
It is close. Our weighted score puts them within 2 points (Mistral Nemo 48/100, Llama-3.1-8B-Instruct 46/100, Mistral Large 2.1 38/100), so choose by what matters most for your work: Mistral Large 2.1 for raw capability and Mistral Nemo on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMistral Large 2.1Capabilities Index (ECI): Mistral Large 2.1 128.5 · Mistral Nemo 118.7 · Llama-3.1-8B-Instruct 116.6
- Lowest priceMistral NemoMistral Nemo $0.15 · Llama-3.1-8B-Instruct $0.156 · Mistral Large 2.1 $3.00 per 1M tokens (3:1 blend)
- Longest contextMistral Large 2.1Mistral Large 2.1 131,072 · Llama-3.1-8B-Instruct 128,000 · Mistral Nemo 128,000 tokens
- Widest inputsSame inputsLlama-3.1-8B-Instruct: Text · Mistral Large 2.1: Text · Mistral Nemo: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama-3.1-8B-Instruct | Mistral Large 2.1 | Mistral Nemo |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 36 | 51 | 39 |
| Price | 25% | 88 | 27 | 89 |
| Inputs & features | 15% | 25 | 25 | 25 |
| Context window | 10% | 24 | 24 | 24 |
| Overall | 100% | 46/100 | 38/100 | 48/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Llama-3.1-8B-InstructMeta | Mistral Large 2.1Mistral AI | Mistral NemoMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 116.6 | 128.5 (best) | 118.7 |
| ECI rank | #145 of 148 | #130 of 148 (best) | #140 of 148 |
| GPQA DiamondGraduate-level science questions | 27.0% | 51.3% (best) | 29.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | 1.7% | 7.8% (best) | — |
| Price per million tokens | |||
| Input | $0.152 | $2.00 | $0.15 (best) |
| Output | $0.167 | $6.00 | $0.15 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.156 | $3.00 | $0.15 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 9 providers | Official Mistral API | Official Mistral API |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 128,000 tokens |
| Max output | 4,096 tokens | 16,384 tokens | 128,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | 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 | Open | Open |
| API model ID | — | mistral-large-2411 | mistral-nemo |
| API providers | 9 (best) | 2 | 5 |
| Released | Jul 23, 2024 | Nov 18, 2024 | Jul 1, 2024 |
| Knowledge cutoff | Dec 2023 | Nov 2024 | Jul 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Llama-3.1-8B-Instruct$1.85
- Mistral Large 2.1$32.00
- Mistral Nemo$1.80
Which should you choose?
Which is better: Llama-3.1-8B-Instruct, Mistral Large 2.1 or Mistral Nemo?
It is close. Our weighted score puts them within 2 points (Mistral Nemo 48/100, Llama-3.1-8B-Instruct 46/100, Mistral Large 2.1 38/100), so choose by what matters most for your work: Mistral Large 2.1 for raw capability and Mistral Nemo on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Llama-3.1-8B-Instruct, Mistral Large 2.1 or Mistral Nemo?
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); Mistral Large 2.1 costs $2.00 input / $6.00 output per million tokens (official Mistral API price). 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 $3.00 for Mistral Large 2.1 (20× as much).
Which scores higher on benchmarks?
Mistral Large 2.1 scores higher on the Capabilities Index (ECI): Mistral Large 2.1 128.5 (#130 of 148), Mistral Nemo 118.7 (#140 of 148) and Llama-3.1-8B-Instruct 116.6 (#145 of 148). Their confidence ranges do not overlap (123.8–130.8 vs 111.3–121.5), so the gap is a real one. On individual benchmarks: GPQA Diamond — Mistral Large 2.1 51.3%, 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 Llama-3.1-8B-Instruct, Mistral Large 2.1 and Mistral Nemo 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?
Mistral Large 2.1 has the largest context window at 131,072 tokens, against 128,000 for Llama-3.1-8B-Instruct and 128,000 for Mistral Nemo. Maximum output per response: Llama-3.1-8B-Instruct up to 4,096, Mistral Large 2.1 up to 16,384, Mistral Nemo up to 128,000 tokens.
Which can read images, PDFs, audio or video?
Llama-3.1-8B-Instruct accepts text; Mistral Large 2.1 accepts text; Mistral Nemo accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Mistral Large 2.1 is the newest, released Nov 18, 2024. Llama-3.1-8B-Instruct came out Jul 23, 2024; Mistral Nemo came out Jul 1, 2024. Knowledge cutoff: Llama-3.1-8B-Instruct Dec 2023, Mistral Large 2.1 Nov 2024, Mistral Nemo Jul 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 Oct 4, 2026.
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