ZMIME
Comparison · 3 models · Updated Oct 4, 2026

GPT-5.4 nano vs MiniMax-M2.7 vs Qwen3.5 Flash

Qwen3.5 Flash comes out ahead, 75 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. MiniMax

    MiniMax-M2.7

    Released Mar 18, 2026

    61/100
    • ECI145.9
    • Price$0.30 / $1.20
    • Context205K
    Full specs Remove MiniMax-M2.7
  3. Our pick

    Alibaba (Qwen)

    Qwen3.5 Flash

    Released Feb 23, 2026

    75/100
    • ECI144.0
    • Price$0.10 / $0.40
    • Context1M
    Full specs Remove Qwen3.5 Flash
01 — Verdict

Qwen3.5 Flash is our pick

Qwen3.5 Flash is the better all-round choice, scoring 75/100 against GPT-5.4 nano (68) and MiniMax-M2.7 (61). It leads on price, inputs & features and 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 Flash 144.0
  • Lowest priceQwen3.5 FlashQwen3.5 Flash $0.175 · GPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 per 1M tokens (3:1 blend)
  • Longest contextQwen3.5 FlashQwen3.5 Flash 1,000,000 · GPT-5.4 nano 400,000 · MiniMax-M2.7 204,800 tokens
  • Widest inputsQwen3.5 FlashGPT-5.4 nano: Text, Images · MiniMax-M2.7: Text · Qwen3.5 Flash: Text, Images, Video
  • Self-hostingMiniMax-M2.7Publishes downloadable weights
How the score is built
MeasureWeightGPT-5.4 nanoMiniMax-M2.7Qwen3.5 Flash
CapabilityCapabilities Index (ECI)50%737371
Price25%666386
Inputs & features15%703580
Context window10%443260
Overall100%68/10061/10075/100
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 MiniMax-M2.7 vs Qwen3.5 Flash specifications side by side
SpecificationGPT-5.4 nanoOpenAIMiniMax-M2.7MiniMaxQwen3.5 FlashAlibaba (Qwen)
Capability
Capabilities Index (ECI)145.8145.9 (best)144.0
ECI rank#75 of 148#73 of 148 (best)#82 of 148
GPQA DiamondGraduate-level science questions78.5%—82.3% (best)
FrontierMath Tiers 1–3Research-level mathematics44.9% (best)—18.3%
OTIS Mock AIME 2024–2025Competition mathematics87.8% (best)—84.4%
SimpleQA VerifiedShort factual questions11.7%—20.3% (best)
Price per million tokens
Input$0.20$0.30$0.10 (best)
Output$1.25$1.20$0.40 (best)
Cached input$0.02$0.06$0.01 (best)
Blended (3:1)$0.463$0.525$0.175 (best)
Long-context rateSame rateSame rateSame rate
Price sourceOfficial OpenAI APIOfficial MiniMax (minimax.io) APIOfficial Alibaba API
Limits
Context window400,000 tokens204,800 tokens1,000,000 tokens (best)
Max output128,000 tokens131,072 tokens (best)65,536 tokens
Inputs and features
TextYesYesYes
ImagesYesNoYes
PDFsNoNoNo
AudioNoNoNo
VideoNoNoYes
ReasoningYeslow · medium · high · xhighYesYes
Tool callingYesYesYes
Structured outputYesNoYes
Availability
WeightsProprietaryOpenProprietary
API model IDgpt-5.4-nanoMiniMax-M2.7qwen3.5-flash
API providers2629 (best)8
ReleasedMar 17, 2026Mar 18, 2026Feb 23, 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
  • MiniMax-M2.7$5.40
  • Qwen3.5 Flash$1.80
04 — Questions

Which should you choose?

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

Qwen3.5 Flash is the better all-round choice, scoring 75/100 against GPT-5.4 nano (68) and MiniMax-M2.7 (61). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

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

Qwen3.5 Flash is cheaper at $0.10 input / $0.40 output per million tokens (official Alibaba API price). 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 $0.175 per million tokens for Qwen3.5 Flash versus $0.463 for GPT-5.4 nano (2.6× as much) and $0.525 for MiniMax-M2.7 (3× 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 Flash 144.0 (#82 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, MiniMax-M2.7 and Qwen3.5 Flash 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?

Qwen3.5 Flash has the largest context window at 1,000,000 tokens, against 400,000 for GPT-5.4 nano and 204,800 for MiniMax-M2.7. Maximum output per response: GPT-5.4 nano up to 128,000, MiniMax-M2.7 up to 131,072, Qwen3.5 Flash up to 65,536 tokens.

Which can read images, PDFs, audio or video?

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

Are any of these open source?

MiniMax-M2.7 publishes its weights and can be self-hosted; GPT-5.4 nano and Qwen3.5 Flash 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 Flash 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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