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Comparison · 3 models · Updated Oct 4, 2026

Llama 4 Maverick 17B Instruct vs QwQ 32B vs DeepSeek-V3

Llama 4 Maverick 17B Instruct comes out ahead, 58 to 53 and 50 on our weighted score, and it is the cheaper option too.

  1. Our pick

    Meta

    Llama 4 Maverick 17B Instruct

    Released Apr 5, 2025

    58/100
    • ECI132.2
    • Price$0.321 / $0.91
    • Context1M
    Full specs Remove Llama 4 Maverick 17B Instruct

  2. Alibaba (Qwen)

    QwQ 32B

    Released Mar 5, 2025

    53/100
    • ECI137.6
    • Price$0.66 / $1.00
    • Context131K
    Full specs Remove QwQ 32B

  3. DeepSeek

    DeepSeek-V3

    Released Dec 26, 2024

    50/100
    • ECI132.3
    • Price$0.32 / $1.10
    • Context131K
    Full specs Remove DeepSeek-V3
01 — Verdict

Llama 4 Maverick 17B Instruct is our pick

Llama 4 Maverick 17B Instruct is the better all-round choice, scoring 58/100 against QwQ 32B (53) and DeepSeek-V3 (50). It leads on price, inputs & features and context window. QwQ 32B wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityQwQ 32BCapabilities Index (ECI): QwQ 32B 137.6 · DeepSeek-V3 132.3 · Llama 4 Maverick 17B Instruct 132.2
  • Lowest priceLlama 4 Maverick 17B InstructLlama 4 Maverick 17B Instruct $0.468 · DeepSeek-V3 $0.515 · QwQ 32B $0.745 per 1M tokens (3:1 blend)
  • Longest contextLlama 4 Maverick 17B InstructLlama 4 Maverick 17B Instruct 1,000,000 · QwQ 32B 131,072 · DeepSeek-V3 131,072 tokens
  • Widest inputsLlama 4 Maverick 17B InstructLlama 4 Maverick 17B Instruct: Text, Images · QwQ 32B: Text · DeepSeek-V3: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightLlama 4 Maverick 17B InstructQwQ 32BDeepSeek-V3
CapabilityCapabilities Index (ECI)50%566256
Price25%665664
Inputs & features15%503525
Context window10%602424
Overall100%58/10053/10050/100
02 — Side by side

Every spec in one table

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

Llama 4 Maverick 17B Instruct vs QwQ 32B vs DeepSeek-V3 specifications side by side
SpecificationLlama 4 Maverick 17B InstructMetaQwQ 32BAlibaba (Qwen)DeepSeek-V3DeepSeek
Capability
Capabilities Index (ECI)132.2137.6 (best)132.3
ECI rank#122 of 148#109 of 148 (best)#121 of 148
GPQA DiamondGraduate-level science questions67.0% (best)65.3%56.5%
OTIS Mock AIME 2024–2025Competition mathematics20.6%59.2% (best)15.8%
Price per million tokens
Input$0.321$0.66$0.32 (best)
Output$0.91 (best)$1.00$1.10
Cached input———
Blended (3:1)$0.468 (best)$0.745$0.515
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 6 providersMedian of 1 providersMedian of 5 providers
Limits
Context window1,000,000 tokens (best)131,072 tokens131,072 tokens
Max output16,384 tokens (best)8,192 tokens8,192 tokens
Inputs and features
TextYesYesYes
ImagesYesNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoYesNo
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenOpenOpenDeepSeek Model License
API model ID———
API providers6 (best)15
ReleasedApr 5, 2025Mar 5, 2025Dec 26, 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 Maverick 17B Instruct$5.03
  • QwQ 32B$8.60
  • DeepSeek-V3$5.40
04 — Questions

Which should you choose?

Which is better: Llama 4 Maverick 17B Instruct, QwQ 32B or DeepSeek-V3?

Llama 4 Maverick 17B Instruct is the better all-round choice, scoring 58/100 against QwQ 32B (53) and DeepSeek-V3 (50). It leads on price, inputs & features and context window. QwQ 32B wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Llama 4 Maverick 17B Instruct, QwQ 32B or DeepSeek-V3?

Llama 4 Maverick 17B Instruct is cheaper at $0.321 input / $0.91 output per million tokens (median across 6 API providers). DeepSeek-V3 costs $0.32 input / $1.10 output per million tokens (median across 5 API providers); QwQ 32B costs $0.66 input / $1.00 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.468 per million tokens for Llama 4 Maverick 17B Instruct versus $0.515 for DeepSeek-V3 (1.1× as much) and $0.745 for QwQ 32B (1.6× as much).

Which scores higher on benchmarks?

QwQ 32B scores higher on the Capabilities Index (ECI): QwQ 32B 137.6 (#109 of 148), DeepSeek-V3 132.3 (#121 of 148) and Llama 4 Maverick 17B Instruct 132.2 (#122 of 148). The confidence ranges of the top two overlap (133.1–141.7 vs 127.5–135.5), so treat the gap as small. On individual benchmarks: GPQA Diamond — Llama 4 Maverick 17B Instruct 67.0%, QwQ 32B 65.3%, DeepSeek-V3 56.5%; OTIS Mock AIME 2024–2025 — QwQ 32B 59.2%, Llama 4 Maverick 17B Instruct 20.6%, DeepSeek-V3 15.8%.

Which is better for coding?

There are no published SWE-bench Verified results for Llama 4 Maverick 17B Instruct, QwQ 32B and DeepSeek-V3 yet, so there is no like-for-like coding score. On overall capability, QwQ 32B leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.

Which has the bigger context window?

Llama 4 Maverick 17B Instruct has the largest context window at 1,000,000 tokens, against 131,072 for QwQ 32B and 131,072 for DeepSeek-V3. Maximum output per response: Llama 4 Maverick 17B Instruct up to 16,384, QwQ 32B up to 8,192, DeepSeek-V3 up to 8,192 tokens.

Which can read images, PDFs, audio or video?

Llama 4 Maverick 17B Instruct accepts text and images; QwQ 32B accepts text; DeepSeek-V3 accepts text. Llama 4 Maverick 17B Instruct handles the widest range of inputs.

Are any of these open source?

Yes, all three publish their weights (DeepSeek Model License), so you can self-host them.

Which is newer?

Llama 4 Maverick 17B Instruct is the newest, released Apr 5, 2025. QwQ 32B came out Mar 5, 2025; DeepSeek-V3 came out Dec 26, 2024. Knowledge cutoff: Llama 4 Maverick 17B Instruct Aug 2024, QwQ 32B 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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