ZMIME
Comparison · 3 models · Updated Oct 4, 2026

GPT-5.4 nano vs MiniMax-M2.7 vs Qwen3 14B

GPT-5.4 nano comes out ahead, 68 to 61 and 54 on our weighted score, and it is the cheaper option too.

  1. 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

  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. Alibaba (Qwen)

    Qwen3 14B

    Released Apr 29, 2025

    54/100
    • ECI138.2
    • Price$0.35 / $1.40
    • Context131K
    Full specs Remove Qwen3 14B
01 — Verdict

GPT-5.4 nano is our pick

GPT-5.4 nano is the better all-round choice, scoring 68/100 against MiniMax-M2.7 (61) and Qwen3 14B (54). 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 14B 138.2
  • Lowest priceGPT-5.4 nanoGPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 · Qwen3 14B $0.613 per 1M tokens (3:1 blend)
  • Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · MiniMax-M2.7 204,800 · Qwen3 14B 131,072 tokens
  • Widest inputsGPT-5.4 nanoGPT-5.4 nano: Text, Images · MiniMax-M2.7: Text · Qwen3 14B: Text
  • Self-hostingMiniMax-M2.7 and Qwen3 14BPublishes downloadable weights
How the score is built
MeasureWeightGPT-5.4 nanoMiniMax-M2.7Qwen3 14B
CapabilityCapabilities Index (ECI)50%737363
Price25%666360
Inputs & features15%703535
Context window10%443224
Overall100%68/10061/10054/100
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 14B specifications side by side
SpecificationGPT-5.4 nanoOpenAIMiniMax-M2.7MiniMaxQwen3 14BAlibaba (Qwen)
Capability
Capabilities Index (ECI)145.8145.9 (best)138.2
ECI rank#75 of 148#73 of 148 (best)#107 of 148
GPQA DiamondGraduate-level science questions78.5% (best)—63.8%
FrontierMath Tiers 1–3Research-level mathematics44.9%——
OTIS Mock AIME 2024–2025Competition mathematics87.8% (best)—66.4%
SimpleQA VerifiedShort factual questions11.7%——
Price per million tokens
Input$0.20 (best)$0.30$0.35
Output$1.25$1.20 (best)$1.40
Cached input$0.02 (best)$0.06—
Blended (3:1)$0.463 (best)$0.525$0.613
Long-context rateSame rateSame rateSame rate
Price sourceOfficial OpenAI APIOfficial MiniMax (minimax.io) APIOfficial Alibaba API
Limits
Context window400,000 tokens (best)204,800 tokens131,072 tokens
Max output128,000 tokens131,072 tokens (best)8,192 tokens
Inputs and features
TextYesYesYes
ImagesYesNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYeslow · medium · high · xhighYesYes
Tool callingYesYesYes
Structured outputYesNoNo
Availability
WeightsProprietaryOpenOpen
API model IDgpt-5.4-nanoMiniMax-M2.7qwen3-14b
API providers2629 (best)1
ReleasedMar 17, 2026Mar 18, 2026Apr 29, 2025
Knowledge cutoffAug 31, 2025—Apr 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 14B$6.30
04 — Questions

Which should you choose?

Which is better: GPT-5.4 nano, MiniMax-M2.7 or Qwen3 14B?

GPT-5.4 nano is the better all-round choice, scoring 68/100 against MiniMax-M2.7 (61) and Qwen3 14B (54). 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 14B?

GPT-5.4 nano is cheaper at $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); Qwen3 14B costs $0.35 input / $1.40 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.463 per million tokens for GPT-5.4 nano versus $0.525 for MiniMax-M2.7 (1.1× as much) and $0.613 for Qwen3 14B (1.3× 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 14B 138.2 (#107 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 14B 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 204,800 for MiniMax-M2.7 and 131,072 for Qwen3 14B. Maximum output per response: GPT-5.4 nano up to 128,000, MiniMax-M2.7 up to 131,072, Qwen3 14B up to 8,192 tokens.

Which can read images, PDFs, audio or video?

GPT-5.4 nano accepts text and images; MiniMax-M2.7 accepts text; Qwen3 14B accepts text. GPT-5.4 nano handles the widest range of inputs.

Are any of these open source?

MiniMax-M2.7 and Qwen3 14B 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 14B came out Apr 29, 2025. Knowledge cutoff: GPT-5.4 nano Aug 31, 2025, Qwen3 14B Apr 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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