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Comparison · 3 models · Updated Oct 4, 2026

GPT-4.1 nano vs Qwen3.5 9B vs Gemma 3 27B IT

Qwen3.5 9B comes out ahead, 72 to 63 and 60 on our weighted score.

  1. OpenAI

    GPT-4.1 nano

    Released Apr 14, 2025Deprecated

    63/100
    • ECI129.6
    • Price$0.10 / $0.40
    • Context1.05M
    Full specs Remove GPT-4.1 nano
  2. Our pick

    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

  3. Google

    Gemma 3 27B IT

    Released Mar 12, 2025

    60/100
    • ECI130.0
    • Price$0.08 / $0.20
    • Context131K
    Full specs Remove Gemma 3 27B IT
01 — Verdict

Qwen3.5 9B is our pick

Qwen3.5 9B is the better all-round choice, scoring 72/100 against GPT-4.1 nano (63) and Gemma 3 27B IT (60). It leads on capability and inputs & features. GPT-4.1 nano wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityQwen3.5 9BCapabilities Index (ECI): Qwen3.5 9B 139.5 · Gemma 3 27B IT 130.0 · GPT-4.1 nano 129.6
  • Lowest priceGemma 3 27B ITGemma 3 27B IT $0.11 · Qwen3.5 9B $0.113 · GPT-4.1 nano $0.175 per 1M tokens (3:1 blend)
  • Longest contextGPT-4.1 nanoGPT-4.1 nano 1,047,576 · Qwen3.5 9B 262,144 · Gemma 3 27B IT 131,072 tokens
  • Widest inputsQwen3.5 9BGPT-4.1 nano: Text, Images · Qwen3.5 9B: Text, Images, Video · Gemma 3 27B IT: Text, Images
  • Self-hostingQwen3.5 9B and Gemma 3 27B ITPublishes downloadable weights
How the score is built
MeasureWeightGPT-4.1 nanoQwen3.5 9BGemma 3 27B IT
CapabilityCapabilities Index (ECI)50%526553
Price25%869595
Inputs & features15%608050
Context window10%613724
Overall100%63/10072/10060/100
02 — Side by side

Every spec in one table

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

GPT-4.1 nano vs Qwen3.5 9B vs Gemma 3 27B IT specifications side by side
SpecificationGPT-4.1 nanoOpenAIQwen3.5 9BAlibaba (Qwen)Gemma 3 27B ITGoogle
Capability
Capabilities Index (ECI)129.6139.5 (best)130.0
ECI rank#127 of 148#101 of 148 (best)#125 of 148
GPQA DiamondGraduate-level science questions48.9%79.0% (best)47.7%
OTIS Mock AIME 2024–2025Competition mathematics28.9%61.7% (best)22.5%
SimpleQA VerifiedShort factual questions6.0%——
Price per million tokens
Input$0.10$0.10$0.08 (best)
Output$0.40$0.15 (best)$0.20
Cached input$0.025——
Blended (3:1)$0.175$0.113$0.11 (best)
Long-context rateSame rateSame rateSame rate
Price sourceOfficial OpenAI APIMedian of 14 providersMedian of 9 providers
Limits
Context window1,047,576 tokens (best)262,144 tokens131,072 tokens
Max output32,768 tokens65,536 tokens131,072 tokens (best)
Inputs and features
TextYesYesYes
ImagesYesYesYes
PDFsNoNoNo
AudioNoNoNo
VideoNoYesNo
ReasoningNoYesNo
Tool callingYesYesYes
Structured outputYesYesNo
Availability
WeightsProprietaryOpenOpen
API model IDgpt-4.1-nano——
API providers20 (best)1510
ReleasedApr 14, 2025Feb 23, 2026Mar 12, 2025
Knowledge cutoffApr 2024—Aug 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-4.1 nano$1.80
  • Qwen3.5 9B$1.30
  • Gemma 3 27B IT$1.20
04 — Questions

Which should you choose?

Which is better: GPT-4.1 nano, Qwen3.5 9B or Gemma 3 27B IT?

Qwen3.5 9B is the better all-round choice, scoring 72/100 against GPT-4.1 nano (63) and Gemma 3 27B IT (60). It leads on capability and inputs & features. GPT-4.1 nano wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, GPT-4.1 nano, Qwen3.5 9B or Gemma 3 27B IT?

Gemma 3 27B IT is cheaper at $0.08 input / $0.20 output per million tokens (median across 9 API providers). Qwen3.5 9B costs $0.10 input / $0.15 output per million tokens (median across 14 API providers); GPT-4.1 nano costs $0.10 input / $0.40 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $0.11 per million tokens for Gemma 3 27B IT versus $0.113 for Qwen3.5 9B (1× as much) and $0.175 for GPT-4.1 nano (1.6× as much).

Which scores higher on benchmarks?

Qwen3.5 9B scores higher on the Capabilities Index (ECI): Qwen3.5 9B 139.5 (#101 of 148), Gemma 3 27B IT 130.0 (#125 of 148) and GPT-4.1 nano 129.6 (#127 of 148). Their confidence ranges do not overlap (136.5–141.3 vs 124.1–132.2), so the gap is a real one. On individual benchmarks: GPQA Diamond — Qwen3.5 9B 79.0%, GPT-4.1 nano 48.9%, Gemma 3 27B IT 47.7%; OTIS Mock AIME 2024–2025 — Qwen3.5 9B 61.7%, GPT-4.1 nano 28.9%, Gemma 3 27B IT 22.5%.

Which is better for coding?

There are no published SWE-bench Verified results for GPT-4.1 nano, Qwen3.5 9B and Gemma 3 27B IT yet, so there is no like-for-like coding score. On overall capability, Qwen3.5 9B 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-4.1 nano has the largest context window at 1,047,576 tokens, against 262,144 for Qwen3.5 9B and 131,072 for Gemma 3 27B IT. Maximum output per response: GPT-4.1 nano up to 32,768, Qwen3.5 9B up to 65,536, Gemma 3 27B IT up to 131,072 tokens.

Which can read images, PDFs, audio or video?

GPT-4.1 nano accepts text and images; Qwen3.5 9B accepts text, images and video; Gemma 3 27B IT accepts text and images. Qwen3.5 9B handles the widest range of inputs.

Are any of these open source?

Qwen3.5 9B and Gemma 3 27B IT publishes its weights and can be self-hosted; GPT-4.1 nano is proprietary.

Which is newer?

Qwen3.5 9B is the newest, released Feb 23, 2026. GPT-4.1 nano came out Apr 14, 2025; Gemma 3 27B IT came out Mar 12, 2025. Knowledge cutoff: GPT-4.1 nano Apr 2024, Gemma 3 27B IT Aug 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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