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Comparison · 2 models · Updated Oct 4, 2026

Llama 4 Scout 17B Instruct vs Qwen2.5 32B Instruct

Llama 4 Scout 17B Instruct comes out ahead, 62 to 43 on our weighted score, and it is the cheaper option too.

  1. Our pick

    Meta

    Llama 4 Scout 17B Instruct

    Released Apr 5, 2025

    62/100
    • ECI129.7
    • Price$0.225 / $0.69
    • Context10M
    Full specs Remove Llama 4 Scout 17B Instruct

  2. Alibaba (Qwen)

    Qwen2.5 32B Instruct

    Released Sep 17, 2024

    43/100
    • ECI128.5
    • Price$0.70 / $2.80
    • Context131K
    Full specs Remove Qwen2.5 32B Instruct

  3. Add a model

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01 — Verdict

Llama 4 Scout 17B Instruct is our pick

Llama 4 Scout 17B Instruct is the better all-round choice, scoring 62/100 against Qwen2.5 32B Instruct (43). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityLlama 4 Scout 17B InstructCapabilities Index (ECI): Llama 4 Scout 17B Instruct 129.7 · Qwen2.5 32B Instruct 128.5
  • Lowest priceLlama 4 Scout 17B InstructLlama 4 Scout 17B Instruct $0.341 · Qwen2.5 32B Instruct $1.23 per 1M tokens (3:1 blend)
  • Longest contextLlama 4 Scout 17B InstructLlama 4 Scout 17B Instruct 10,000,000 · Qwen2.5 32B Instruct 131,072 tokens
  • Widest inputsLlama 4 Scout 17B InstructLlama 4 Scout 17B Instruct: Text, Images · Qwen2.5 32B Instruct: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightLlama 4 Scout 17B InstructQwen2.5 32B Instruct
CapabilityCapabilities Index (ECI)50%5251
Price25%7246
Inputs & features15%5025
Context window10%10024
Overall100%62/10043/100
02 — Side by side

Every spec in one table

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

Llama 4 Scout 17B Instruct vs Qwen2.5 32B Instruct specifications side by side
SpecificationLlama 4 Scout 17B InstructMetaQwen2.5 32B InstructAlibaba (Qwen)
Capability
Capabilities Index (ECI)129.7 (best)128.5
ECI rank#126 of 148 (best)#131 of 148
GPQA DiamondGraduate-level science questions51.8% (best)46.1%
OTIS Mock AIME 2024–2025Competition mathematics7.8% (best)7.4%
Price per million tokens
Input$0.225 (best)$0.70
Output$0.69 (best)$2.80
Cached input——
Blended (3:1)$0.341 (best)$1.23
Long-context rateSame rateSame rate
Price sourceMedian of 4 providersOfficial Alibaba API
Limits
Context window10,000,000 tokens (best)131,072 tokens
Max output16,384 tokens (best)8,192 tokens
Inputs and features
TextYesYes
ImagesYesNo
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningNoNo
Tool callingYesYes
Structured outputNoNo
Availability
WeightsOpenOpen
API model ID—qwen2-5-32b-instruct
API providers4 (best)1
ReleasedApr 5, 2025Sep 17, 2024
Knowledge cutoffAug 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 4 Scout 17B Instruct$3.63
  • Qwen2.5 32B Instruct$12.60
04 — Questions

Which should you choose?

Which is better: Llama 4 Scout 17B Instruct or Qwen2.5 32B Instruct?

Llama 4 Scout 17B Instruct is the better all-round choice, scoring 62/100 against Qwen2.5 32B Instruct (43). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Llama 4 Scout 17B Instruct or Qwen2.5 32B Instruct?

Llama 4 Scout 17B Instruct is cheaper at $0.225 input / $0.69 output per million tokens (median across 4 API providers). Qwen2.5 32B Instruct costs $0.70 input / $2.80 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.341 per million tokens for Llama 4 Scout 17B Instruct versus $1.23 for Qwen2.5 32B Instruct (3.6× as much).

Which scores higher on benchmarks?

Llama 4 Scout 17B Instruct scores higher on the Capabilities Index (ECI): Llama 4 Scout 17B Instruct 129.7 (#126 of 148) and Qwen2.5 32B Instruct 128.5 (#131 of 148). The confidence ranges of the top two overlap (124.8–131.4 vs 123.5–130.0), so treat the gap as small. On individual benchmarks: GPQA Diamond — Llama 4 Scout 17B Instruct 51.8%, Qwen2.5 32B Instruct 46.1%; OTIS Mock AIME 2024–2025 — Llama 4 Scout 17B Instruct 7.8%, Qwen2.5 32B Instruct 7.4%.

Which is better for coding?

There are no published SWE-bench Verified results for Llama 4 Scout 17B Instruct and Qwen2.5 32B Instruct yet, so there is no like-for-like coding score. On overall capability, Llama 4 Scout 17B 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?

Llama 4 Scout 17B Instruct has the largest context window at 10,000,000 tokens, against 131,072 for Qwen2.5 32B Instruct. Maximum output per response: Llama 4 Scout 17B Instruct up to 16,384, Qwen2.5 32B Instruct up to 8,192 tokens.

Which can read images, PDFs, audio or video?

Llama 4 Scout 17B Instruct accepts text and images; Qwen2.5 32B Instruct accepts text. Llama 4 Scout 17B Instruct handles the widest range of inputs.

Are any of these open source?

Yes, both publish their weights, so you can self-host them.

Which is newer?

Llama 4 Scout 17B Instruct is the newest, released Apr 5, 2025. Qwen2.5 32B Instruct came out Sep 17, 2024. Knowledge cutoff: Llama 4 Scout 17B Instruct Aug 2024, Qwen2.5 32B 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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