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Comparison · 3 models · Updated Oct 4, 2026

MiniMax-M2.7 vs Qwen3 8B vs GPT-5.4 nano

GPT-5.4 nano comes out ahead, 68 to 61 and 56 on our weighted score, though Qwen3 8B is 33% cheaper per token.

  1. MiniMax

    MiniMax-M2.7

    Released Mar 18, 2026

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

  2. Alibaba (Qwen)

    Qwen3 8B

    Released Apr 28, 2025

    56/100
    • ECI136.2
    • Price$0.18 / $0.70
    • Context131K
    Full specs Remove Qwen3 8B
  3. 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
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 8B (56). It leads on inputs & features and context window. Qwen3 8B wins on price. 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 8B 136.2
  • Lowest priceQwen3 8BQwen3 8B $0.31 · 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 · MiniMax-M2.7 204,800 · Qwen3 8B 131,072 tokens
  • Widest inputsGPT-5.4 nanoMiniMax-M2.7: Text · Qwen3 8B: Text · GPT-5.4 nano: Text, Images
  • Self-hostingMiniMax-M2.7 and Qwen3 8BPublishes downloadable weights
How the score is built
MeasureWeightMiniMax-M2.7Qwen3 8BGPT-5.4 nano
CapabilityCapabilities Index (ECI)50%736173
Price25%637466
Inputs & features15%353570
Context window10%322444
Overall100%61/10056/10068/100
02 — Side by side

Every spec in one table

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

MiniMax-M2.7 vs Qwen3 8B vs GPT-5.4 nano specifications side by side
SpecificationMiniMax-M2.7MiniMaxQwen3 8BAlibaba (Qwen)GPT-5.4 nanoOpenAI
Capability
Capabilities Index (ECI)145.9 (best)136.2145.8
ECI rank#73 of 148 (best)#113 of 148#75 of 148
GPQA DiamondGraduate-level science questions—56.8%78.5% (best)
FrontierMath Tiers 1–3Research-level mathematics——44.9%
OTIS Mock AIME 2024–2025Competition mathematics—56.1%87.8% (best)
SimpleQA VerifiedShort factual questions——11.7%
Price per million tokens
Input$0.30$0.18 (best)$0.20
Output$1.20$0.70 (best)$1.25
Cached input$0.06—$0.02 (best)
Blended (3:1)$0.525$0.31 (best)$0.463
Long-context rateSame rateSame rateSame rate
Price sourceOfficial MiniMax (minimax.io) APIOfficial Alibaba APIOfficial OpenAI API
Limits
Context window204,800 tokens131,072 tokens400,000 tokens (best)
Max output131,072 tokens (best)8,192 tokens128,000 tokens
Inputs and features
TextYesYesYes
ImagesNoNoYes
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesYeslow · medium · high · xhigh
Tool callingYesYesYes
Structured outputNoNoYes
Availability
WeightsOpenOpenProprietary
API model IDMiniMax-M2.7qwen3-8bgpt-5.4-nano
API providers29 (best)126
ReleasedMar 18, 2026Apr 28, 2025Mar 17, 2026
Knowledge cutoff—Apr 2025Aug 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.

  • MiniMax-M2.7$5.40
  • Qwen3 8B$3.20
  • GPT-5.4 nano$4.50
04 — Questions

Which should you choose?

Which is better: MiniMax-M2.7, Qwen3 8B or GPT-5.4 nano?

GPT-5.4 nano is the better all-round choice, scoring 68/100 against MiniMax-M2.7 (61) and Qwen3 8B (56). It leads on inputs & features and context window. Qwen3 8B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, MiniMax-M2.7, Qwen3 8B or GPT-5.4 nano?

Qwen3 8B is cheaper at $0.18 input / $0.70 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 $0.31 per million tokens for Qwen3 8B versus $0.463 for GPT-5.4 nano (1.5× as much) and $0.525 for MiniMax-M2.7 (1.7× 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 8B 136.2 (#113 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 MiniMax-M2.7, Qwen3 8B and GPT-5.4 nano 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 8B. Maximum output per response: MiniMax-M2.7 up to 131,072, Qwen3 8B up to 8,192, GPT-5.4 nano up to 128,000 tokens.

Which can read images, PDFs, audio or video?

MiniMax-M2.7 accepts text; Qwen3 8B accepts text; GPT-5.4 nano accepts text and images. GPT-5.4 nano handles the widest range of inputs.

Are any of these open source?

MiniMax-M2.7 and Qwen3 8B 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 8B came out Apr 28, 2025. Knowledge cutoff: Qwen3 8B Apr 2025, 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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