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Comparison · 3 models · Updated Oct 4, 2026

DeepSeek V3.2 vs Qwen3 8B vs GPT-5.4 nano

GPT-5.4 nano comes out ahead, 68 to 64 and 56 on our weighted score, though Qwen3 8B is 33% cheaper per token.

  1. DeepSeek

    DeepSeek V3.2

    Released Dec 1, 2025

    64/100
    • ECI146.3
    • Price$0.296 / $0.48
    • Context128K
    Full specs Remove DeepSeek V3.2

  2. Alibaba (Qwen)

    Qwen3 8B

    Released Apr 28, 2025

    56/100
    • ECI136.2
    • Price$0.18 / $0.70
    • Context131K
    Full specs Remove Qwen3 8B
  3. Our pick

    OpenAI

    GPT-5.4 nano

    Released Mar 17, 2026

    68/100
    • ECI145.8
    • Price$0.20 / $1.25
    • Context400K
    Full specs Remove GPT-5.4 nano
01 — Verdict

GPT-5.4 nano is our pick

GPT-5.4 nano is the better all-round choice, scoring 68/100 against DeepSeek V3.2 (64) and Qwen3 8B (56). It leads on inputs & features and context window. Qwen3 8B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityDeepSeek V3.2Capabilities Index (ECI): DeepSeek V3.2 146.3 · GPT-5.4 nano 145.8 · Qwen3 8B 136.2
  • Lowest priceQwen3 8BQwen3 8B $0.31 · DeepSeek V3.2 $0.342 · GPT-5.4 nano $0.463 per 1M tokens (3:1 blend)
  • Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · Qwen3 8B 131,072 · DeepSeek V3.2 128,000 tokens
  • Widest inputsGPT-5.4 nanoDeepSeek V3.2: Text · Qwen3 8B: Text · GPT-5.4 nano: Text, Images
  • Self-hostingDeepSeek V3.2 and Qwen3 8BPublishes downloadable weights (MIT License)
How the score is built
MeasureWeightDeepSeek V3.2Qwen3 8BGPT-5.4 nano
CapabilityCapabilities Index (ECI)50%736173
Price25%727466
Inputs & features15%453570
Context window10%242444
Overall100%64/10056/10068/100
02 — Side by side

Every spec in one table

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

DeepSeek V3.2 vs Qwen3 8B vs GPT-5.4 nano specifications side by side
SpecificationDeepSeek V3.2DeepSeekQwen3 8BAlibaba (Qwen)GPT-5.4 nanoOpenAI
Capability
Capabilities Index (ECI)146.3 (best)136.2145.8
ECI rank#69 of 148 (best)#113 of 148#75 of 148
GPQA DiamondGraduate-level science questions83.4% (best)56.8%78.5%
FrontierMath Tiers 1–3Research-level mathematics——44.9%
OTIS Mock AIME 2024–2025Competition mathematics87.8% (best)56.1%87.8%
SimpleQA VerifiedShort factual questions——11.7%
Price per million tokens
Input$0.296$0.18 (best)$0.20
Output$0.48 (best)$0.70$1.25
Cached input——$0.02
Blended (3:1)$0.342$0.31 (best)$0.463
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 15 providersOfficial Alibaba APIOfficial OpenAI API
Limits
Context window128,000 tokens131,072 tokens400,000 tokens (best)
Max output64,000 tokens8,192 tokens128,000 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoNoYes
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesYeslow · medium · high · xhigh
Tool callingYesYesYes
Structured outputYesNoYes
Availability
WeightsOpenMIT LicenseOpenProprietary
API model ID—qwen3-8bgpt-5.4-nano
API providers15126 (best)
ReleasedDec 1, 2025Apr 28, 2025Mar 17, 2026
Knowledge cutoffJul 2024Apr 2025Aug 31, 2025
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.

  • DeepSeek V3.2$3.92
  • Qwen3 8B$3.20
  • GPT-5.4 nano$4.50
04 — Questions

Which should you choose?

Which is better: DeepSeek V3.2, Qwen3 8B or GPT-5.4 nano?

GPT-5.4 nano is the better all-round choice, scoring 68/100 against DeepSeek V3.2 (64) and Qwen3 8B (56). It leads on inputs & features and context window. Qwen3 8B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, DeepSeek V3.2, Qwen3 8B or GPT-5.4 nano?

Qwen3 8B is cheaper at $0.18 input / $0.70 output per million tokens (official Alibaba API price). DeepSeek V3.2 costs $0.296 input / $0.48 output per million tokens (median across 15 API providers); GPT-5.4 nano costs $0.20 input / $1.25 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $0.31 per million tokens for Qwen3 8B versus $0.342 for DeepSeek V3.2 (1.1× as much) and $0.463 for GPT-5.4 nano (1.5× as much).

Which scores higher on benchmarks?

DeepSeek V3.2 scores higher on the Capabilities Index (ECI): DeepSeek V3.2 146.3 (#69 of 148), GPT-5.4 nano 145.8 (#75 of 148) and Qwen3 8B 136.2 (#113 of 148). The confidence ranges of the top two overlap (144.4–147.5 vs 143.2–147.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — DeepSeek V3.2 83.4%, GPT-5.4 nano 78.5%, Qwen3 8B 56.8%; OTIS Mock AIME 2024–2025 — DeepSeek V3.2 87.8%, GPT-5.4 nano 87.8%, Qwen3 8B 56.1%.

Which is better for coding?

There are no published SWE-bench Verified results for DeepSeek V3.2, Qwen3 8B and GPT-5.4 nano yet, so there is no like-for-like coding score. On overall capability, DeepSeek V3.2 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?

GPT-5.4 nano has the largest context window at 400,000 tokens, against 131,072 for Qwen3 8B and 128,000 for DeepSeek V3.2. Maximum output per response: DeepSeek V3.2 up to 64,000, Qwen3 8B up to 8,192, GPT-5.4 nano up to 128,000 tokens.

Which can read images, PDFs, audio or video?

DeepSeek V3.2 accepts text; Qwen3 8B accepts text; GPT-5.4 nano accepts text and images. GPT-5.4 nano handles the widest range of inputs.

Are any of these open source?

DeepSeek V3.2 and Qwen3 8B publishes its weights (MIT License) and can be self-hosted; GPT-5.4 nano is proprietary.

Which is newer?

GPT-5.4 nano is the newest, released Mar 17, 2026. DeepSeek V3.2 came out Dec 1, 2025; Qwen3 8B came out Apr 28, 2025. Knowledge cutoff: DeepSeek V3.2 Jul 2024, Qwen3 8B Apr 2025, GPT-5.4 nano Aug 31, 2025.

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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