ZMIME
Comparison · 3 models · Updated Oct 4, 2026

Llama-3.1-8B-Instruct vs Mistral Nemo vs Qwen2.5 7B Instruct

Too close to call on our weighted score (Mistral Nemo 48, Llama-3.1-8B-Instruct 46, Qwen2.5 7B Instruct 44). The right pick depends on what you value most.

  1. 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

  2. Mistral AI

    Mistral Nemo

    Released Jul 1, 2024

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

  3. Alibaba (Qwen)

    Qwen2.5 7B Instruct

    Released Sep 19, 2024

    44/100
    • ECI118.5
    • Price$0.175 / $0.70
    • Context131K
    Full specs Remove Qwen2.5 7B 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, Qwen2.5 7B Instruct 44/100), so choose by what matters most for your work: Mistral Nemo for raw capability and Qwen2.5 7B Instruct for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityMistral NemoCapabilities Index (ECI): Mistral Nemo 118.7 · Qwen2.5 7B Instruct 118.5 · Llama-3.1-8B-Instruct 116.6
  • Lowest priceMistral NemoMistral Nemo $0.15 · Llama-3.1-8B-Instruct $0.156 · Qwen2.5 7B Instruct $0.306 per 1M tokens (3:1 blend)
  • Longest contextQwen2.5 7B InstructQwen2.5 7B Instruct 131,072 · Llama-3.1-8B-Instruct 128,000 · Mistral Nemo 128,000 tokens
  • Widest inputsSame inputsLlama-3.1-8B-Instruct: Text · Mistral Nemo: Text · Qwen2.5 7B Instruct: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightLlama-3.1-8B-InstructMistral NemoQwen2.5 7B Instruct
CapabilityCapabilities Index (ECI)50%363938
Price25%888974
Inputs & features15%252525
Context window10%242424
Overall100%46/10048/10044/100
02 — Side by side

Every spec in one table

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

Llama-3.1-8B-Instruct vs Mistral Nemo vs Qwen2.5 7B Instruct specifications side by side
SpecificationLlama-3.1-8B-InstructMetaMistral NemoMistral AIQwen2.5 7B InstructAlibaba (Qwen)
Capability
Capabilities Index (ECI)116.6118.7 (best)118.5
ECI rank#145 of 148#140 of 148 (best)#141 of 148
GPQA DiamondGraduate-level science questions27.0%29.9%35.5% (best)
OTIS Mock AIME 2024–2025Competition mathematics1.7%—2.5% (best)
Price per million tokens
Input$0.152$0.15 (best)$0.175
Output$0.167$0.15 (best)$0.70
Cached input———
Blended (3:1)$0.156$0.15 (best)$0.306
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 9 providersOfficial Mistral APIOfficial Alibaba API
Limits
Context window128,000 tokens128,000 tokens131,072 tokens (best)
Max output4,096 tokens128,000 tokens (best)8,192 tokens
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoNoNo
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenOpenOpen
API model ID—mistral-nemoqwen2-5-7b-instruct
API providers9 (best)51
ReleasedJul 23, 2024Jul 1, 2024Sep 19, 2024
Knowledge cutoffDec 2023Jul 2024Apr 2024
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.

  • Llama-3.1-8B-Instruct$1.85
  • Mistral Nemo$1.80
  • Qwen2.5 7B Instruct$3.15
04 — Questions

Which should you choose?

Which is better: Llama-3.1-8B-Instruct, Mistral Nemo or Qwen2.5 7B Instruct?

It is close. Our weighted score puts them within 2 points (Mistral Nemo 48/100, Llama-3.1-8B-Instruct 46/100, Qwen2.5 7B Instruct 44/100), so choose by what matters most for your work: Mistral Nemo for raw capability and Qwen2.5 7B Instruct for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Llama-3.1-8B-Instruct, Mistral Nemo or Qwen2.5 7B 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); Qwen2.5 7B Instruct costs $0.175 input / $0.70 output per million tokens (official Alibaba 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 $0.306 for Qwen2.5 7B Instruct (2× as much).

Which scores higher on benchmarks?

Mistral Nemo scores higher on the Capabilities Index (ECI): Mistral Nemo 118.7 (#140 of 148), Qwen2.5 7B Instruct 118.5 (#141 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 110.7–121.3), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen2.5 7B Instruct 35.5%, 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 Nemo and Qwen2.5 7B 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?

Qwen2.5 7B Instruct 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 Nemo up to 128,000, Qwen2.5 7B Instruct up to 8,192 tokens.

Which can read images, PDFs, audio or video?

Llama-3.1-8B-Instruct accepts text; Mistral Nemo accepts text; Qwen2.5 7B 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?

Qwen2.5 7B Instruct is the newest, released Sep 19, 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 Nemo Jul 2024, Qwen2.5 7B Instruct Apr 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.

05 — Keep comparing

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