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Comparison · 3 models · Updated Oct 4, 2026

GPT-5 Nano vs DeepSeek-V3.1 vs Qwen3.5 9B

Too close to call on our weighted score (Qwen3.5 9B 72, GPT-5 Nano 70, DeepSeek-V3.1 55). The right pick depends on what you value most.

  1. OpenAI

    GPT-5 Nano

    Released Aug 7, 2025

    70/100
    • ECI139.4
    • Price$0.05 / $0.40
    • Context400K
    Full specs Remove GPT-5 Nano

  2. DeepSeek

    DeepSeek-V3.1

    Released Aug 21, 2025

    55/100
    • ECI139.9
    • Price$0.385 / $1.25
    • Context131K
    Full specs Remove DeepSeek-V3.1

  3. Alibaba (Qwen)

    Qwen3.5 9B

    Released Feb 23, 2026

    72/100
    • ECI139.5
    • Price$0.10 / $0.15
    • Context262K
    Full specs Remove Qwen3.5 9B
01 — Verdict

Too close to call

It is close. Our weighted score puts them within 2 points (Qwen3.5 9B 72/100, GPT-5 Nano 70/100, DeepSeek-V3.1 55/100), so choose by what matters most for your work: DeepSeek-V3.1 for raw capability, Qwen3.5 9B on price and GPT-5 Nano for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityDeepSeek-V3.1Capabilities Index (ECI): DeepSeek-V3.1 139.9 · Qwen3.5 9B 139.5 · GPT-5 Nano 139.4
  • Lowest priceQwen3.5 9BQwen3.5 9B $0.113 · GPT-5 Nano $0.138 · DeepSeek-V3.1 $0.601 per 1M tokens (3:1 blend)
  • Longest contextGPT-5 NanoGPT-5 Nano 400,000 · Qwen3.5 9B 262,144 · DeepSeek-V3.1 131,072 tokens
  • Widest inputsQwen3.5 9BGPT-5 Nano: Text, Images · DeepSeek-V3.1: Text · Qwen3.5 9B: Text, Images, Video
  • Self-hostingDeepSeek-V3.1 and Qwen3.5 9BPublishes downloadable weights (MIT License)
How the score is built
MeasureWeightGPT-5 NanoDeepSeek-V3.1Qwen3.5 9B
CapabilityCapabilities Index (ECI)50%656565
Price25%916095
Inputs & features15%703580
Context window10%442437
Overall100%70/10055/10072/100
02 — Side by side

Every spec in one table

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

GPT-5 Nano vs DeepSeek-V3.1 vs Qwen3.5 9B specifications side by side
SpecificationGPT-5 NanoOpenAIDeepSeek-V3.1DeepSeekQwen3.5 9BAlibaba (Qwen)
Capability
Capabilities Index (ECI)139.4139.9 (best)139.5
ECI rank#102 of 148#100 of 148 (best)#101 of 148
GPQA DiamondGraduate-level science questions69.4%—79.0% (best)
FrontierMath Tiers 1–3Research-level mathematics20.0%——
OTIS Mock AIME 2024–2025Competition mathematics81.1% (best)—61.7%
SimpleQA VerifiedShort factual questions11.7%——
Price per million tokens
Input$0.05 (best)$0.385$0.10
Output$0.40$1.25$0.15 (best)
Cached input$0.005——
Blended (3:1)$0.138$0.601$0.113 (best)
Long-context rateSame rateSame rateSame rate
Price sourceOfficial OpenAI APIMedian of 8 providersMedian of 14 providers
Limits
Context window400,000 tokens (best)131,072 tokens262,144 tokens
Max output128,000 tokens (best)8,192 tokens65,536 tokens
Inputs and features
TextYesYesYes
ImagesYesNoYes
PDFsNoNoNo
AudioNoNoNo
VideoNoNoYes
ReasoningYesminimal · low · medium · highYesYes
Tool callingYesYesYes
Structured outputYesNoYes
Availability
WeightsProprietaryOpenMIT LicenseOpen
API model IDgpt-5-nano——
API providers21 (best)815
ReleasedAug 7, 2025Aug 21, 2025Feb 23, 2026
Knowledge cutoffMay 30, 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.

  • GPT-5 Nano$1.30
  • DeepSeek-V3.1$6.35
  • Qwen3.5 9B$1.30
04 — Questions

Which should you choose?

Which is better: GPT-5 Nano, DeepSeek-V3.1 or Qwen3.5 9B?

It is close. Our weighted score puts them within 2 points (Qwen3.5 9B 72/100, GPT-5 Nano 70/100, DeepSeek-V3.1 55/100), so choose by what matters most for your work: DeepSeek-V3.1 for raw capability, Qwen3.5 9B on price and GPT-5 Nano for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, GPT-5 Nano, DeepSeek-V3.1 or Qwen3.5 9B?

Qwen3.5 9B is cheaper at $0.10 input / $0.15 output per million tokens (median across 14 API providers). GPT-5 Nano costs $0.05 input / $0.40 output per million tokens (official OpenAI API price); DeepSeek-V3.1 costs $0.385 input / $1.25 output per million tokens (median across 8 API providers). At a typical mix of three input tokens to one output token, that is $0.113 per million tokens for Qwen3.5 9B versus $0.138 for GPT-5 Nano (1.2× as much) and $0.601 for DeepSeek-V3.1 (5.3× as much).

Which scores higher on benchmarks?

DeepSeek-V3.1 scores higher on the Capabilities Index (ECI): DeepSeek-V3.1 139.9 (#100 of 148), Qwen3.5 9B 139.5 (#101 of 148) and GPT-5 Nano 139.4 (#102 of 148). The confidence ranges of the top two overlap (136.1–143.3 vs 136.5–141.3), so treat the gap as small.

Which is better for coding?

There are no published SWE-bench Verified results for GPT-5 Nano, DeepSeek-V3.1 and Qwen3.5 9B yet, so there is no like-for-like coding score. On overall capability, DeepSeek-V3.1 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 Nano has the largest context window at 400,000 tokens, against 262,144 for Qwen3.5 9B and 131,072 for DeepSeek-V3.1. Maximum output per response: GPT-5 Nano up to 128,000, DeepSeek-V3.1 up to 8,192, Qwen3.5 9B up to 65,536 tokens.

Which can read images, PDFs, audio or video?

GPT-5 Nano accepts text and images; DeepSeek-V3.1 accepts text; Qwen3.5 9B accepts text, images and video. Qwen3.5 9B handles the widest range of inputs.

Are any of these open source?

DeepSeek-V3.1 and Qwen3.5 9B publishes its weights (MIT License) and can be self-hosted; GPT-5 Nano is proprietary.

Which is newer?

Qwen3.5 9B is the newest, released Feb 23, 2026. DeepSeek-V3.1 came out Aug 21, 2025; GPT-5 Nano came out Aug 7, 2025. Knowledge cutoff: GPT-5 Nano May 30, 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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