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Comparison · 2 models · Updated Nov 2, 2026

Mixtral 8x7B Instruct v0.1 vs Llama-3.1-8B-Instruct

Llama-3.1-8B-Instruct comes out ahead, 49 to 34 on our weighted score, and it is the cheaper option too.

  1. Mistral AI

    Mixtral 8x7B Instruct v0.1

    Released Dec 11, 2023

    34/100
    • ECI118.4
    • Price$0.444 / $0.579
    • Context33K
    Full specs Remove Mixtral 8x7B Instruct v0.1
  2. Our pick

    Meta

    Llama-3.1-8B-Instruct

    Released Jul 23, 2024

    49/100
    • ECI116.4
    • Price$0.06 / $0.08
    • Context128K
    Full specs Remove Llama-3.1-8B-Instruct

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Llama-3.1-8B-Instruct is our pick

Llama-3.1-8B-Instruct is the better all-round choice, scoring 49/100 against Mixtral 8x7B Instruct v0.1 (34). It leads on price, inputs & features and context window. Mixtral 8x7B Instruct v0.1 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityMixtral 8x7B Instruct v0.1Capabilities Index (ECI): Mixtral 8x7B Instruct v0.1 118.4 · Llama-3.1-8B-Instruct 116.4
  • Lowest priceLlama-3.1-8B-InstructLlama-3.1-8B-Instruct $0.065 · Mixtral 8x7B Instruct v0.1 $0.478 per 1M tokens (3:1 blend)
  • Longest contextLlama-3.1-8B-InstructLlama-3.1-8B-Instruct 128,000 · Mixtral 8x7B Instruct v0.1 32,768 tokens
  • Widest inputsSame inputsMixtral 8x7B Instruct v0.1: 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
MeasureWeightMixtral 8x7B Instruct v0.1Llama-3.1-8B-Instruct
CapabilityCapabilities Index (ECI)50%3835
Price25%62100
Inputs & features15%025
Context window10%024
Overall100%34/10049/100
02 — Side by side

Every spec in one table

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

Mixtral 8x7B Instruct v0.1 vs Llama-3.1-8B-Instruct specifications side by side
SpecificationMixtral 8x7B Instruct v0.1Mistral AILlama-3.1-8B-InstructMeta
Capability
Capabilities Index (ECI)118.4 (best)116.4
ECI rank#160 of 165 (best)#162 of 165
GPQA DiamondGraduate-level science questions30.6% (best)27.0%
OTIS Mock AIME 2024–2025Competition mathematics—1.7%
Price per million tokens
Input$0.444$0.06 (best)
Output$0.579$0.08 (best)
Cached input——
Blended (3:1)$0.478$0.065 (best)
Long-context rateSame rateSame rate
Price sourceMedian of 2 providersMedian of 17 providers
Limits
Context window32,768 tokens128,000 tokens (best)
Max output32,768 tokens (best)4,096 tokens
Inputs and features
TextYesYes
ImagesNoNo
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningNoNo
Tool callingNoYes
Structured outputNoNo
Availability
WeightsOpenApache 2.0Open
API model ID——
API providers218 (best)
ReleasedDec 11, 2023Jul 23, 2024
Knowledge cutoff—Dec 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.

  • Mixtral 8x7B Instruct v0.1$5.60
  • Llama-3.1-8B-Instruct$0.76
04 — Questions

Which should you choose?

Which is better: Mixtral 8x7B Instruct v0.1 or Llama-3.1-8B-Instruct?

Llama-3.1-8B-Instruct is the better all-round choice, scoring 49/100 against Mixtral 8x7B Instruct v0.1 (34). It leads on price, inputs & features and context window. Mixtral 8x7B Instruct v0.1 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Mixtral 8x7B Instruct v0.1 or Llama-3.1-8B-Instruct?

Llama-3.1-8B-Instruct is cheaper at $0.06 input / $0.08 output per million tokens (median across 17 API providers). Mixtral 8x7B Instruct v0.1 costs $0.444 input / $0.579 output per million tokens (median across 2 API providers). At a typical mix of three input tokens to one output token, that is $0.065 per million tokens for Llama-3.1-8B-Instruct versus $0.478 for Mixtral 8x7B Instruct v0.1 (7.4× as much).

Which scores higher on benchmarks?

Mixtral 8x7B Instruct v0.1 scores higher on the Capabilities Index (ECI): Mixtral 8x7B Instruct v0.1 118.4 (#160 of 165) and Llama-3.1-8B-Instruct 116.4 (#162 of 165). The confidence ranges of the top two overlap (110.9–121.0 vs 107.0–121.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — Mixtral 8x7B Instruct v0.1 30.6%, Llama-3.1-8B-Instruct 27.0%.

Which is better for coding?

There are no published SWE-bench Verified results for Mixtral 8x7B Instruct v0.1 and Llama-3.1-8B-Instruct yet, so there is no like-for-like coding score. On overall capability, Mixtral 8x7B Instruct v0.1 leads, which tends to carry over to coding, but test on your own codebase. Note that Mixtral 8x7B Instruct v0.1 does not support tool calling, which most coding agents need.

Which has the bigger context window?

Llama-3.1-8B-Instruct has the largest context window at 128,000 tokens, against 32,768 for Mixtral 8x7B Instruct v0.1. Maximum output per response: Mixtral 8x7B Instruct v0.1 up to 32,768, Llama-3.1-8B-Instruct up to 4,096 tokens.

Which can read images, PDFs, audio or video?

Mixtral 8x7B Instruct v0.1 accepts text; Llama-3.1-8B-Instruct accepts text. They handle the same number of input types.

Are any of these open source?

Yes, both publish their weights (Apache 2.0), so you can self-host them.

Which is newer?

Llama-3.1-8B-Instruct is the newest, released Jul 23, 2024. Mixtral 8x7B Instruct v0.1 came out Dec 11, 2023. Knowledge cutoff: 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 Nov 2, 2026.

05 — Keep comparing

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