ZMIME
Comparison · 3 models · Updated Oct 4, 2026

Mistral Nemo vs Mistral Large 2.1 vs Llama-3.1-8B-Instruct

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.

  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

    Mistral Large 2.1

    Released Nov 18, 2024

    38/100
    • ECI128.5
    • Price$2.00 / $6.00
    • Context131K
    Full specs Remove Mistral Large 2.1

  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, 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 · Mistral Nemo 128,000 · Llama-3.1-8B-Instruct 128,000 tokens
  • Widest inputsSame inputsMistral Nemo: Text · Mistral Large 2.1: 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 NemoMistral Large 2.1Llama-3.1-8B-Instruct
CapabilityCapabilities Index (ECI)50%395136
Price25%892788
Inputs & features15%252525
Context window10%242424
Overall100%48/10038/10046/100
02 — Side by side

Every spec in one table

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

Mistral Nemo vs Mistral Large 2.1 vs Llama-3.1-8B-Instruct specifications side by side
SpecificationMistral NemoMistral AIMistral Large 2.1Mistral AILlama-3.1-8B-InstructMeta
Capability
Capabilities Index (ECI)118.7128.5 (best)116.6
ECI rank#140 of 148#130 of 148 (best)#145 of 148
GPQA DiamondGraduate-level science questions29.9%51.3% (best)27.0%
OTIS Mock AIME 2024–2025Competition mathematics—7.8% (best)1.7%
Price per million tokens
Input$0.15 (best)$2.00$0.152
Output$0.15 (best)$6.00$0.167
Cached input———
Blended (3:1)$0.15 (best)$3.00$0.156
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Mistral APIOfficial Mistral APIMedian of 9 providers
Limits
Context window128,000 tokens131,072 tokens (best)128,000 tokens
Max output128,000 tokens (best)16,384 tokens4,096 tokens
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoNoNo
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenOpenOpen
API model IDmistral-nemomistral-large-2411—
API providers529 (best)
ReleasedJul 1, 2024Nov 18, 2024Jul 23, 2024
Knowledge cutoffJul 2024Nov 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
  • Mistral Large 2.1$32.00
  • Llama-3.1-8B-Instruct$1.85
04 — Questions

Which should you choose?

Which is better: Mistral Nemo, Mistral Large 2.1 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, 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, Mistral Nemo, Mistral Large 2.1 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); 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 Mistral Nemo, Mistral Large 2.1 and Llama-3.1-8B-Instruct 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 Mistral Nemo and 128,000 for Llama-3.1-8B-Instruct. Maximum output per response: Mistral Nemo up to 128,000, Mistral Large 2.1 up to 16,384, Llama-3.1-8B-Instruct up to 4,096 tokens.

Which can read images, PDFs, audio or video?

Mistral Nemo accepts text; Mistral Large 2.1 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?

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: Mistral Nemo Jul 2024, Mistral Large 2.1 Nov 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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