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Comparison · 2 models · Updated Oct 4, 2026

Llama-3.3-70B-Instruct vs Qwen2.5 32B Instruct

Too close to call on our weighted score (Llama-3.3-70B-Instruct 46, Qwen2.5 32B Instruct 43). The right pick depends on what you value most.

  1. Meta

    Llama-3.3-70B-Instruct

    Released Dec 6, 2024

    46/100
    • ECI127.3
    • Price$0.59 / $0.724
    • Context128K
    Full specs Remove Llama-3.3-70B-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

Too close to call

It is close. Our weighted score puts them within 3 points (Llama-3.3-70B-Instruct 46/100, Qwen2.5 32B Instruct 43/100), so choose by what matters most for your work: Qwen2.5 32B Instruct for raw capability and Llama-3.3-70B-Instruct on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityQwen2.5 32B InstructCapabilities Index (ECI): Qwen2.5 32B Instruct 128.5 · Llama-3.3-70B-Instruct 127.3
  • Lowest priceLlama-3.3-70B-InstructLlama-3.3-70B-Instruct $0.624 · Qwen2.5 32B Instruct $1.23 per 1M tokens (3:1 blend)
  • Longest contextQwen2.5 32B InstructQwen2.5 32B Instruct 131,072 · Llama-3.3-70B-Instruct 128,000 tokens
  • Widest inputsSame inputsLlama-3.3-70B-Instruct: Text · 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-3.3-70B-InstructQwen2.5 32B Instruct
CapabilityCapabilities Index (ECI)50%4951
Price25%6046
Inputs & features15%2525
Context window10%2424
Overall100%46/10043/100
02 — Side by side

Every spec in one table

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

Llama-3.3-70B-Instruct vs Qwen2.5 32B Instruct specifications side by side
SpecificationLlama-3.3-70B-InstructMetaQwen2.5 32B InstructAlibaba (Qwen)
Capability
Capabilities Index (ECI)127.3128.5 (best)
ECI rank#133 of 148#131 of 148 (best)
GPQA DiamondGraduate-level science questions47.4% (best)46.1%
OTIS Mock AIME 2024–2025Competition mathematics5.1%7.4% (best)
Price per million tokens
Input$0.59 (best)$0.70
Output$0.724 (best)$2.80
Cached input——
Blended (3:1)$0.624 (best)$1.23
Long-context rateSame rateSame rate
Price sourceMedian of 21 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 IDllama-3.3-70b-instructqwen2-5-32b-instruct
API providers24 (best)1
ReleasedDec 6, 2024Sep 17, 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-3.3-70B-Instruct$7.35
  • Qwen2.5 32B Instruct$12.60
04 — Questions

Which should you choose?

Which is better: Llama-3.3-70B-Instruct or Qwen2.5 32B Instruct?

It is close. Our weighted score puts them within 3 points (Llama-3.3-70B-Instruct 46/100, Qwen2.5 32B Instruct 43/100), so choose by what matters most for your work: Qwen2.5 32B Instruct for raw capability and Llama-3.3-70B-Instruct on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Llama-3.3-70B-Instruct or Qwen2.5 32B Instruct?

Llama-3.3-70B-Instruct is cheaper at $0.59 input / $0.724 output per million tokens (median across 21 API providers; free on Llama). 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 $0.624 per million tokens for Llama-3.3-70B-Instruct versus $1.23 for Qwen2.5 32B Instruct (2× as much).

Which scores higher on benchmarks?

Qwen2.5 32B Instruct scores higher on the Capabilities Index (ECI): Qwen2.5 32B Instruct 128.5 (#131 of 148) and Llama-3.3-70B-Instruct 127.3 (#133 of 148). The confidence ranges of the top two overlap (123.5–130.0 vs 122.5–129.5), so treat the gap as small. On individual benchmarks: GPQA Diamond — Llama-3.3-70B-Instruct 47.4%, Qwen2.5 32B Instruct 46.1%; OTIS Mock AIME 2024–2025 — Qwen2.5 32B Instruct 7.4%, Llama-3.3-70B-Instruct 5.1%.

Which is better for coding?

There are no published SWE-bench Verified results for Llama-3.3-70B-Instruct and Qwen2.5 32B Instruct yet, so there is no like-for-like coding score. On overall capability, Qwen2.5 32B 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 32B Instruct has the largest context window at 131,072 tokens, against 128,000 for Llama-3.3-70B-Instruct. Maximum output per response: Llama-3.3-70B-Instruct up to 4,096, Qwen2.5 32B Instruct up to 8,192 tokens.

Which can read images, PDFs, audio or video?

Llama-3.3-70B-Instruct accepts text; Qwen2.5 32B 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?

Llama-3.3-70B-Instruct is the newest, released Dec 6, 2024. Qwen2.5 32B Instruct came out Sep 17, 2024. Knowledge cutoff: Llama-3.3-70B-Instruct Dec 2023, 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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