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Comparison · 2 models · Updated Oct 4, 2026

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

Too close to call on our weighted score (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. 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

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01 — Verdict

Too close to call

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

  • CapabilityQwen2.5 7B InstructCapabilities Index (ECI): Qwen2.5 7B Instruct 118.5 · Llama-3.1-8B-Instruct 116.6
  • Lowest priceLlama-3.1-8B-InstructLlama-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 tokens
  • Widest inputsSame inputsLlama-3.1-8B-Instruct: 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-InstructQwen2.5 7B Instruct
CapabilityCapabilities Index (ECI)50%3638
Price25%8874
Inputs & features15%2525
Context window10%2424
Overall100%46/10044/100
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 Qwen2.5 7B Instruct specifications side by side
SpecificationLlama-3.1-8B-InstructMetaQwen2.5 7B InstructAlibaba (Qwen)
Capability
Capabilities Index (ECI)116.6118.5 (best)
ECI rank#145 of 148#141 of 148 (best)
GPQA DiamondGraduate-level science questions27.0%35.5% (best)
OTIS Mock AIME 2024–2025Competition mathematics1.7%2.5% (best)
Price per million tokens
Input$0.152 (best)$0.175
Output$0.167 (best)$0.70
Cached input——
Blended (3:1)$0.156 (best)$0.306
Long-context rateSame rateSame rate
Price sourceMedian of 9 providersOfficial Alibaba API
Limits
Context window128,000 tokens131,072 tokens (best)
Max output4,096 tokens8,192 tokens (best)
Inputs and features
TextYesYes
ImagesNoNo
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningNoNo
Tool callingYesYes
Structured outputNoNo
Availability
WeightsOpenOpen
API model ID—qwen2-5-7b-instruct
API providers9 (best)1
ReleasedJul 23, 2024Sep 19, 2024
Knowledge cutoffDec 2023Apr 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
  • Qwen2.5 7B Instruct$3.15
04 — Questions

Which should you choose?

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

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

Which is cheaper, Llama-3.1-8B-Instruct or Qwen2.5 7B Instruct?

Llama-3.1-8B-Instruct is cheaper at $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 $0.156 per million tokens for Llama-3.1-8B-Instruct versus $0.306 for Qwen2.5 7B Instruct (2× as much).

Which scores higher on benchmarks?

Qwen2.5 7B Instruct scores higher on the Capabilities Index (ECI): 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 (110.7–121.3 vs 106.3–121.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen2.5 7B Instruct 35.5%, Llama-3.1-8B-Instruct 27.0%; OTIS Mock AIME 2024–2025 — Qwen2.5 7B Instruct 2.5%, Llama-3.1-8B-Instruct 1.7%.

Which is better for coding?

There are no published SWE-bench Verified results for Llama-3.1-8B-Instruct and Qwen2.5 7B Instruct yet, so there is no like-for-like coding score. On overall capability, Qwen2.5 7B Instruct leads, which tends to carry over to coding, but test on your own codebase. Both 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. Maximum output per response: Llama-3.1-8B-Instruct up to 4,096, Qwen2.5 7B Instruct up to 8,192 tokens.

Which can read images, PDFs, audio or video?

Llama-3.1-8B-Instruct accepts text; Qwen2.5 7B Instruct accepts text. They handle the same number of input types.

Are any of these open source?

Yes, both 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. Knowledge cutoff: Llama-3.1-8B-Instruct Dec 2023, 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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