Llama-3.1-8B-Instruct vs Llama 4 Scout 17B Instruct vs Mistral Nemo
Llama 4 Scout 17B Instruct comes out ahead, 62 to 48 and 46 on our weighted score, though Mistral Nemo is 2.3× cheaper per token.
Meta
Llama-3.1-8B-Instruct
46/100- ECI116.6
- Price$0.152 / $0.167
- Context128K
- Our pick
Meta
Llama 4 Scout 17B Instruct
62/100- ECI129.7
- Price$0.225 / $0.69
- Context10M
Mistral AI
Mistral Nemo
48/100- ECI118.7
- Price$0.15 / $0.15
- Context128K
Llama 4 Scout 17B Instruct is our pick
Llama 4 Scout 17B Instruct is the better all-round choice, scoring 62/100 against Mistral Nemo (48) and Llama-3.1-8B-Instruct (46). It leads on capability, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityLlama 4 Scout 17B InstructCapabilities Index (ECI): Llama 4 Scout 17B Instruct 129.7 · Mistral Nemo 118.7 · Llama-3.1-8B-Instruct 116.6
- Lowest priceMistral NemoMistral Nemo $0.15 · Llama-3.1-8B-Instruct $0.156 · Llama 4 Scout 17B Instruct $0.341 per 1M tokens (3:1 blend)
- Longest contextLlama 4 Scout 17B InstructLlama 4 Scout 17B Instruct 10,000,000 · Llama-3.1-8B-Instruct 128,000 · Mistral Nemo 128,000 tokens
- Widest inputsLlama 4 Scout 17B InstructLlama-3.1-8B-Instruct: Text · Llama 4 Scout 17B Instruct: Text, Images · 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 | Llama 4 Scout 17B Instruct | Mistral Nemo |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 36 | 52 | 39 |
| Price | 25% | 88 | 72 | 89 |
| Inputs & features | 15% | 25 | 50 | 25 |
| Context window | 10% | 24 | 100 | 24 |
| Overall | 100% | 46/100 | 62/100 | 48/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 116.6 | 129.7 (best) | 118.7 |
| ECI rank | #145 of 148 | #126 of 148 (best) | #140 of 148 |
| GPQA DiamondGraduate-level science questions | 27.0% | 51.8% (best) | 29.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | 1.7% | 7.8% (best) | — |
| Price per million tokens | |||
| Input | $0.152 | $0.225 | $0.15 (best) |
| Output | $0.167 | $0.69 | $0.15 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.156 | $0.341 | $0.15 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 9 providers | Median of 4 providers | Official Mistral API |
| Limits | |||
| Context window | 128,000 tokens | 10,000,000 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 | Yes | 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-nemo |
| API providers | 9 (best) | 4 | 5 |
| Released | Jul 23, 2024 | Apr 5, 2025 | Jul 1, 2024 |
| Knowledge cutoff | Dec 2023 | Aug 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
Llama 4 Scout 17B Instruct$3.63
Mistral Nemo$1.80
Which should you choose?
Which is better: Llama-3.1-8B-Instruct, Llama 4 Scout 17B Instruct or Mistral Nemo?
Llama 4 Scout 17B Instruct is the better all-round choice, scoring 62/100 against Mistral Nemo (48) and Llama-3.1-8B-Instruct (46). It leads on capability, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Llama-3.1-8B-Instruct, Llama 4 Scout 17B Instruct 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); Llama 4 Scout 17B Instruct costs $0.225 input / $0.69 output per million tokens (median across 4 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 $0.341 for Llama 4 Scout 17B Instruct (2.3× as much).
Which scores higher on benchmarks?
Llama 4 Scout 17B Instruct scores higher on the Capabilities Index (ECI): Llama 4 Scout 17B Instruct 129.7 (#126 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 (124.8–131.4 vs 111.3–121.5), so the gap is a real one. On individual benchmarks: GPQA Diamond — Llama 4 Scout 17B Instruct 51.8%, 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, Llama 4 Scout 17B Instruct and Mistral Nemo yet, so there is no like-for-like coding score. On overall capability, Llama 4 Scout 17B Instruct 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?
Llama 4 Scout 17B Instruct has the largest context window at 10,000,000 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, Llama 4 Scout 17B Instruct 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; Llama 4 Scout 17B Instruct accepts text and images; Mistral Nemo accepts text. Llama 4 Scout 17B Instruct handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Llama 4 Scout 17B Instruct is the newest, released Apr 5, 2025. 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, Llama 4 Scout 17B Instruct Aug 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.