ZMIME
Comparison · 2 models · Updated Nov 2, 2026

Llama-3.2-1B vs Qwen2.5 7B Instruct

Qwen2.5 7B Instruct comes out ahead, 41 to 34 on our weighted score, and it is the cheaper option too.

  1. Meta

    Llama-3.2-1B

    Released Sep 25, 2024

    34/100
    • ECI—
    • Price$0.027 / $0.201
    • Context131K
    Full specs Remove Llama-3.2-1B
  2. Our pick

    Alibaba (Qwen)

    Qwen2.5 7B Instruct

    Released Sep 19, 2024

    41/100
    • ECI118.5
    • Price$0.05 / $0.10
    • Context131K
    Full specs Remove Qwen2.5 7B Instruct

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Qwen2.5 7B Instruct is our pick

Qwen2.5 7B Instruct is the better all-round choice, scoring 41/100 against Llama-3.2-1B (34). It leads on capability and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because Llama-3.2-1B has no Capabilities Index score yet.

  • CapabilityQwen2.5 7B InstructShared benchmarks: Qwen2.5 7B Instruct 19.0% · Llama-3.2-1B 12.2%
  • Lowest priceQwen2.5 7B InstructQwen2.5 7B Instruct $0.063 · Llama-3.2-1B $0.07 per 1M tokens (3:1 blend)
  • Longest contextAbout the sameLlama-3.2-1B 131,072 · Qwen2.5 7B Instruct 131,072 tokens
  • Widest inputsSame inputsLlama-3.2-1B: 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-3.2-1BQwen2.5 7B Instruct
CapabilityShared benchmarks50%1219
Price25%100100
Inputs & features15%025
Context window10%2525
Overall100%34/10041/100
02 — Side by side

Every spec in one table

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

Llama-3.2-1B vs Qwen2.5 7B Instruct specifications side by side
SpecificationLlama-3.2-1BMetaQwen2.5 7B InstructAlibaba (Qwen)
Capability
Capabilities Index (ECI)—118.5
ECI rank—#159 of 165
GPQA DiamondGraduate-level science questions23.9%35.5% (best)
OTIS Mock AIME 2024–2025Competition mathematics0.6%2.5% (best)
Price per million tokens
Input$0.027 (best)$0.05
Output$0.201$0.10 (best)
Cached input——
Blended (3:1)$0.07$0.063 (best)
Long-context rateSame rateSame rate
Price sourceMedian of 6 providersMedian of 3 providers
Limits
Context window131,072 tokens131,072 tokens
Max output8,192 tokens8,192 tokens
Inputs and features
TextYesYes
ImagesNoNo
PDFsNoNo
AudioNoNo
VideoNoNo
ReasoningNoNo
Tool callingNoYes
Structured outputNoNo
Availability
WeightsOpenLlama 3.2 Community LicenseOpenApache 2.0
API model ID——
API providers6 (best)4
ReleasedSep 25, 2024Sep 19, 2024
Knowledge cutoffDec 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.

  • Llama-3.2-1B$0.672
  • Qwen2.5 7B Instruct$0.70
04 — Questions

Which should you choose?

Which is better: Llama-3.2-1B or Qwen2.5 7B Instruct?

Qwen2.5 7B Instruct is the better all-round choice, scoring 41/100 against Llama-3.2-1B (34). It leads on capability and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because Llama-3.2-1B has no Capabilities Index score yet.

Which is cheaper, Llama-3.2-1B or Qwen2.5 7B Instruct?

Qwen2.5 7B Instruct is cheaper at $0.05 input / $0.10 output per million tokens (median across 3 API providers). Llama-3.2-1B costs $0.027 input / $0.201 output per million tokens (median across 6 API providers). At a typical mix of three input tokens to one output token, that is $0.063 per million tokens for Qwen2.5 7B Instruct versus $0.07 for Llama-3.2-1B (1.1× as much).

Which scores higher on benchmarks?

Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025): Qwen2.5 7B Instruct 19.0% and Llama-3.2-1B 12.2%. On individual benchmarks: GPQA Diamond — Qwen2.5 7B Instruct 35.5%, Llama-3.2-1B 23.9%; OTIS Mock AIME 2024–2025 — Qwen2.5 7B Instruct 2.5%, Llama-3.2-1B 0.6%.

Which is better for coding?

There are no published SWE-bench Verified results for Llama-3.2-1B 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. Note that Llama-3.2-1B does not support tool calling, which most coding agents need.

Which has the bigger context window?

Llama-3.2-1B and Qwen2.5 7B Instruct share the same 131,072-token context window. Maximum output per response: Llama-3.2-1B up to 8,192, Qwen2.5 7B Instruct up to 8,192 tokens.

Which can read images, PDFs, audio or video?

Llama-3.2-1B 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 (Llama 3.2 Community License and Apache 2.0), so you can self-host them.

Which is newer?

Llama-3.2-1B is the newest, released Sep 25, 2024. Qwen2.5 7B Instruct came out Sep 19, 2024. Knowledge cutoff: Llama-3.2-1B 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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