ZMIME
Comparison · 3 models · Updated Oct 4, 2026

GLM-4.7 vs Nemotron 3 Ultra 550B A55B vs Kimi K2 Thinking

Nemotron 3 Ultra 550B A55B comes out ahead, 62 to 58 and 56 on our weighted score.

  1. Z.ai (Zhipu)

    GLM-4.7

    Released Dec 22, 2025

    56/100
    • ECI143.5
    • Price$0.60 / $2.20
    • Context205K
    Full specs Remove GLM-4.7
  2. Our pick

    NVIDIA

    Nemotron 3 Ultra 550B A55B

    Released Jun 4, 2026

    62/100
    • ECI146.2
    • Price$0.50 / $2.50
    • Context1M
    Full specs Remove Nemotron 3 Ultra 550B A55B

  3. Moonshot AI

    Kimi K2 Thinking

    Released Nov 6, 2025

    58/100
    • ECI146.0
    • Price$0.60 / $2.50
    • Context262K
    Full specs Remove Kimi K2 Thinking
01 — Verdict

Nemotron 3 Ultra 550B A55B is our pick

Nemotron 3 Ultra 550B A55B is the better all-round choice, scoring 62/100 against Kimi K2 Thinking (58) and GLM-4.7 (56). It leads on inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityNemotron 3 Ultra 550B A55BCapabilities Index (ECI): Nemotron 3 Ultra 550B A55B 146.2 · Kimi K2 Thinking 146.0 · GLM-4.7 143.5
  • Lowest priceGLM-4.7 and Nemotron 3 Ultra 550B A55BGLM-4.7 $1.00 · Nemotron 3 Ultra 550B A55B $1.00 · Kimi K2 Thinking $1.07 per 1M tokens (3:1 blend)
  • Longest contextNemotron 3 Ultra 550B A55BNemotron 3 Ultra 550B A55B 1,000,000 · Kimi K2 Thinking 262,144 · GLM-4.7 204,800 tokens
  • Widest inputsSame inputsGLM-4.7: Text · Nemotron 3 Ultra 550B A55B: Text · Kimi K2 Thinking: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightGLM-4.7Nemotron 3 Ultra 550B A55BKimi K2 Thinking
CapabilityCapabilities Index (ECI)50%707373
Price25%505048
Inputs & features15%354535
Context window10%326037
Overall100%56/10062/10058/100
02 — Side by side

Every spec in one table

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

GLM-4.7 vs Nemotron 3 Ultra 550B A55B vs Kimi K2 Thinking specifications side by side
SpecificationGLM-4.7Z.ai (Zhipu)Nemotron 3 Ultra 550B A55BNVIDIAKimi K2 ThinkingMoonshot AI
Capability
Capabilities Index (ECI)143.5146.2 (best)146.0
ECI rank#84 of 148#70 of 148 (best)#72 of 148
GPQA DiamondGraduate-level science questions83.3%85.4% (best)84.2%
OTIS Mock AIME 2024–2025Competition mathematics83.3%86.7% (best)83.1%
SimpleQA VerifiedShort factual questions32.2%——
Price per million tokens
Input$0.60$0.50 (best)$0.60
Output$2.20 (best)$2.50$2.50
Cached input$0.11 (best)$0.15—
Blended (3:1)$1.00 (best)$1.00 (best)$1.07
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Z.AI APIOfficial Nvidia APIMedian of 10 providers
Limits
Context window204,800 tokens1,000,000 tokens (best)262,144 tokens
Max output131,072 tokens128,000 tokens262,144 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoYesNo
Availability
WeightsOpenOpenOpen
API model IDglm-4.7nvidia/nemotron-3-ultra-550b-a55b—
API providers2021 (best)10
ReleasedDec 22, 2025Jun 4, 2026Nov 6, 2025
Knowledge cutoffApr 2025—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.

  • GLM-4.7$10.40
  • Nemotron 3 Ultra 550B A55B$10.00
  • Kimi K2 Thinking$11.00
04 — Questions

Which should you choose?

Which is better: GLM-4.7, Nemotron 3 Ultra 550B A55B or Kimi K2 Thinking?

Nemotron 3 Ultra 550B A55B is the better all-round choice, scoring 62/100 against Kimi K2 Thinking (58) and GLM-4.7 (56). It leads on inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, GLM-4.7, Nemotron 3 Ultra 550B A55B or Kimi K2 Thinking?

GLM-4.7 is cheaper at $0.60 input / $2.20 output per million tokens (official Z.AI API price). Nemotron 3 Ultra 550B A55B costs $0.50 input / $2.50 output per million tokens (official Nvidia API price); Kimi K2 Thinking costs $0.60 input / $2.50 output per million tokens (median across 10 API providers). At a typical mix of three input tokens to one output token, that is $1.00 per million tokens for GLM-4.7 versus $1.00 for Nemotron 3 Ultra 550B A55B (1× as much) and $1.07 for Kimi K2 Thinking (1.1× as much).

Which scores higher on benchmarks?

Nemotron 3 Ultra 550B A55B scores higher on the Capabilities Index (ECI): Nemotron 3 Ultra 550B A55B 146.2 (#70 of 148), Kimi K2 Thinking 146.0 (#72 of 148) and GLM-4.7 143.5 (#84 of 148). The confidence ranges of the top two overlap (143.9–148.1 vs 143.4–147.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — Nemotron 3 Ultra 550B A55B 85.4%, Kimi K2 Thinking 84.2%, GLM-4.7 83.3%; OTIS Mock AIME 2024–2025 — Nemotron 3 Ultra 550B A55B 86.7%, GLM-4.7 83.3%, Kimi K2 Thinking 83.1%.

Which is better for coding?

There are no published SWE-bench Verified results for GLM-4.7, Nemotron 3 Ultra 550B A55B and Kimi K2 Thinking yet, so there is no like-for-like coding score. On overall capability, Nemotron 3 Ultra 550B A55B 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?

Nemotron 3 Ultra 550B A55B has the largest context window at 1,000,000 tokens, against 262,144 for Kimi K2 Thinking and 204,800 for GLM-4.7. Maximum output per response: GLM-4.7 up to 131,072, Nemotron 3 Ultra 550B A55B up to 128,000, Kimi K2 Thinking up to 262,144 tokens.

Which can read images, PDFs, audio or video?

GLM-4.7 accepts text; Nemotron 3 Ultra 550B A55B accepts text; Kimi K2 Thinking accepts text. They handle the same number of input types.

Are any of these open source?

Yes, all three publish their weights, so you can self-host them.

Which is newer?

Nemotron 3 Ultra 550B A55B is the newest, released Jun 4, 2026. GLM-4.7 came out Dec 22, 2025; Kimi K2 Thinking came out Nov 6, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, Kimi K2 Thinking 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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