ZMIME
Comparison · 3 models · Updated Oct 4, 2026

GPT-5.4 nano vs Qwen3.5 9B vs MiniMax-M2.7

Qwen3.5 9B comes out ahead, 72 to 68 and 61 on our weighted score, and it is the cheaper option too.

  1. 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
  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. MiniMax

    MiniMax-M2.7

    Released Mar 18, 2026

    61/100
    • ECI145.9
    • Price$0.30 / $1.20
    • Context205K
    Full specs Remove MiniMax-M2.7
01 — Verdict

Qwen3.5 9B is our pick

Qwen3.5 9B is the better all-round choice, scoring 72/100 against GPT-5.4 nano (68) and MiniMax-M2.7 (61). It leads on price and inputs & features. GPT-5.4 nano wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityMiniMax-M2.7Capabilities Index (ECI): MiniMax-M2.7 145.9 · GPT-5.4 nano 145.8 · Qwen3.5 9B 139.5
  • Lowest priceQwen3.5 9BQwen3.5 9B $0.113 · GPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 per 1M tokens (3:1 blend)
  • Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · Qwen3.5 9B 262,144 · MiniMax-M2.7 204,800 tokens
  • Widest inputsQwen3.5 9BGPT-5.4 nano: Text, Images · Qwen3.5 9B: Text, Images, Video · MiniMax-M2.7: Text
  • Self-hostingQwen3.5 9B and MiniMax-M2.7Publishes downloadable weights
How the score is built
MeasureWeightGPT-5.4 nanoQwen3.5 9BMiniMax-M2.7
CapabilityCapabilities Index (ECI)50%736573
Price25%669563
Inputs & features15%708035
Context window10%443732
Overall100%68/10072/10061/100
02 — Side by side

Every spec in one table

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

GPT-5.4 nano vs Qwen3.5 9B vs MiniMax-M2.7 specifications side by side
SpecificationGPT-5.4 nanoOpenAIQwen3.5 9BAlibaba (Qwen)MiniMax-M2.7MiniMax
Capability
Capabilities Index (ECI)145.8139.5145.9 (best)
ECI rank#75 of 148#101 of 148#73 of 148 (best)
GPQA DiamondGraduate-level science questions78.5%79.0% (best)—
FrontierMath Tiers 1–3Research-level mathematics44.9%——
OTIS Mock AIME 2024–2025Competition mathematics87.8% (best)61.7%—
SimpleQA VerifiedShort factual questions11.7%——
Price per million tokens
Input$0.20$0.10 (best)$0.30
Output$1.25$0.15 (best)$1.20
Cached input$0.02 (best)—$0.06
Blended (3:1)$0.463$0.113 (best)$0.525
Long-context rateSame rateSame rateSame rate
Price sourceOfficial OpenAI APIMedian of 14 providersOfficial MiniMax (minimax.io) API
Limits
Context window400,000 tokens (best)262,144 tokens204,800 tokens
Max output128,000 tokens65,536 tokens131,072 tokens (best)
Inputs and features
TextYesYesYes
ImagesYesYesNo
PDFsNoNoNo
AudioNoNoNo
VideoNoYesNo
ReasoningYeslow · medium · high · xhighYesYes
Tool callingYesYesYes
Structured outputYesYesNo
Availability
WeightsProprietaryOpenOpen
API model IDgpt-5.4-nano—MiniMax-M2.7
API providers261529 (best)
ReleasedMar 17, 2026Feb 23, 2026Mar 18, 2026
Knowledge cutoffAug 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.

  • GPT-5.4 nano$4.50
  • Qwen3.5 9B$1.30
  • MiniMax-M2.7$5.40
04 — Questions

Which should you choose?

Which is better: GPT-5.4 nano, Qwen3.5 9B or MiniMax-M2.7?

Qwen3.5 9B is the better all-round choice, scoring 72/100 against GPT-5.4 nano (68) and MiniMax-M2.7 (61). It leads on price and inputs & features. GPT-5.4 nano wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, GPT-5.4 nano, Qwen3.5 9B or MiniMax-M2.7?

Qwen3.5 9B is cheaper at $0.10 input / $0.15 output per million tokens (median across 14 API providers). GPT-5.4 nano costs $0.20 input / $1.25 output per million tokens (official OpenAI API price); MiniMax-M2.7 costs $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). At a typical mix of three input tokens to one output token, that is $0.113 per million tokens for Qwen3.5 9B versus $0.463 for GPT-5.4 nano (4.1× as much) and $0.525 for MiniMax-M2.7 (4.7× as much).

Which scores higher on benchmarks?

MiniMax-M2.7 scores higher on the Capabilities Index (ECI): MiniMax-M2.7 145.9 (#73 of 148), GPT-5.4 nano 145.8 (#75 of 148) and Qwen3.5 9B 139.5 (#101 of 148). The confidence ranges of the top two overlap (138.2–148.0 vs 143.2–147.7), so treat the gap as small.

Which is better for coding?

There are no published SWE-bench Verified results for GPT-5.4 nano, Qwen3.5 9B and MiniMax-M2.7 yet, so there is no like-for-like coding score. On overall capability, MiniMax-M2.7 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 262,144 for Qwen3.5 9B and 204,800 for MiniMax-M2.7. Maximum output per response: GPT-5.4 nano up to 128,000, Qwen3.5 9B up to 65,536, MiniMax-M2.7 up to 131,072 tokens.

Which can read images, PDFs, audio or video?

GPT-5.4 nano accepts text and images; Qwen3.5 9B accepts text, images and video; MiniMax-M2.7 accepts text. Qwen3.5 9B handles the widest range of inputs.

Are any of these open source?

Qwen3.5 9B and MiniMax-M2.7 publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.

Which is newer?

MiniMax-M2.7 is the newest, released Mar 18, 2026. GPT-5.4 nano came out Mar 17, 2026; Qwen3.5 9B came out Feb 23, 2026. Knowledge cutoff: 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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