ZMIME
Comparison · 3 models · Updated Oct 4, 2026

Mistral Nemo vs Mixtral 8x7B vs Llama-3.1-8B-Instruct

Too close to call on our weighted score (Mistral Nemo 48, Llama-3.1-8B-Instruct 46, Mixtral 8x7B 37). The right pick depends on what you value most.

  1. Mistral AI

    Mistral Nemo

    Released Jul 1, 2024

    48/100
    • ECI118.7
    • Price$0.15 / $0.15
    • Context128K
    Full specs Remove Mistral Nemo

  2. Mistral AI

    Mixtral 8x7B

    Released Dec 11, 2023

    37/100
    • ECI118.5
    • Price$0.70 / $0.70
    • Context32K
    Full specs Remove Mixtral 8x7B

  3. Meta

    Llama-3.1-8B-Instruct

    Released Jul 23, 2024

    46/100
    • ECI116.6
    • Price$0.152 / $0.167
    • Context128K
    Full specs Remove Llama-3.1-8B-Instruct
01 — Verdict

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, Mixtral 8x7B 37/100), so choose by what matters most for your work: Mistral Nemo for raw capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityMistral NemoCapabilities Index (ECI): Mistral Nemo 118.7 · Mixtral 8x7B 118.5 · Llama-3.1-8B-Instruct 116.6
  • Lowest priceMistral NemoMistral Nemo $0.15 · Llama-3.1-8B-Instruct $0.156 · Mixtral 8x7B $0.70 per 1M tokens (3:1 blend)
  • Longest contextMistral Nemo and Llama-3.1-8B-InstructMistral Nemo 128,000 · Llama-3.1-8B-Instruct 128,000 · Mixtral 8x7B 32,000 tokens
  • Widest inputsSame inputsMistral Nemo: Text · Mixtral 8x7B: Text · Llama-3.1-8B-Instruct: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightMistral NemoMixtral 8x7BLlama-3.1-8B-Instruct
CapabilityCapabilities Index (ECI)50%393836
Price25%895788
Inputs & features15%252525
Context window10%24024
Overall100%48/10037/10046/100
02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Mistral Nemo vs Mixtral 8x7B vs Llama-3.1-8B-Instruct specifications side by side
SpecificationMistral NemoMistral AIMixtral 8x7BMistral AILlama-3.1-8B-InstructMeta
Capability
Capabilities Index (ECI)118.7 (best)118.5116.6
ECI rank#140 of 148 (best)#142 of 148#145 of 148
GPQA DiamondGraduate-level science questions29.9%30.6% (best)27.0%
OTIS Mock AIME 2024–2025Competition mathematics——1.7%
Price per million tokens
Input$0.15 (best)$0.70$0.152
Output$0.15 (best)$0.70$0.167
Cached input———
Blended (3:1)$0.15 (best)$0.70$0.156
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Mistral APIOfficial Mistral APIMedian of 9 providers
Limits
Context window128,000 tokens (best)32,000 tokens128,000 tokens (best)
Max output128,000 tokens (best)32,000 tokens4,096 tokens
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoNoNo
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenOpenOpen
API model IDmistral-nemoopen-mixtral-8x7b—
API providers519 (best)
ReleasedJul 1, 2024Dec 11, 2023Jul 23, 2024
Knowledge cutoffJul 2024Jan 2024Dec 2023
03 — Cost

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
  • Mixtral 8x7B$8.40
  • Llama-3.1-8B-Instruct$1.85
04 — Questions

Which should you choose?

Which is better: Mistral Nemo, Mixtral 8x7B or Llama-3.1-8B-Instruct?

It is close. Our weighted score puts them within 2 points (Mistral Nemo 48/100, Llama-3.1-8B-Instruct 46/100, Mixtral 8x7B 37/100), so choose by what matters most for your work: Mistral Nemo for raw capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Mistral Nemo, Mixtral 8x7B 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); Mixtral 8x7B costs $0.70 input / $0.70 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 $0.70 for Mixtral 8x7B (4.7× as much).

Which scores higher on benchmarks?

Mistral Nemo scores higher on the Capabilities Index (ECI): Mistral Nemo 118.7 (#140 of 148), Mixtral 8x7B 118.5 (#142 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 111.3–121.3), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mixtral 8x7B 30.6%, 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, Mixtral 8x7B 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?

Mistral Nemo and Llama-3.1-8B-Instruct have the largest context windows (128,000 and 128,000 tokens), against 32,000 for Mixtral 8x7B. Maximum output per response: Mistral Nemo up to 128,000, Mixtral 8x7B up to 32,000, Llama-3.1-8B-Instruct up to 4,096 tokens.

Which can read images, PDFs, audio or video?

Mistral Nemo accepts text; Mixtral 8x7B accepts text; Llama-3.1-8B-Instruct 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?

Llama-3.1-8B-Instruct is the newest, released Jul 23, 2024. Mistral Nemo came out Jul 1, 2024; Mixtral 8x7B came out Dec 11, 2023. Knowledge cutoff: Mistral Nemo Jul 2024, Mixtral 8x7B Jan 2024, 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.

05 — Keep comparing

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