ZMIME
Comparison · 2 models · Updated Oct 4, 2026

GLM-4.7-Flash vs GPT-6 Luna

GPT-6 Luna comes out ahead, 86 to 48 on our weighted score, though GLM-4.7-Flash is 27% cheaper per token.

  1. Z.ai (Zhipu)

    GLM-4.7-Flash

    Released Jan 19, 2026

    48/100
    • ECI—
    • Price$0.06 / $0.40
    • Context200K
    Full specs Remove GLM-4.7-Flash
  2. Our pick

    OpenAI

    GPT-6 Luna

    Released Sep 22, 2026

    86/100
    • ECI—
    • Price$0.10 / $0.50
    • Context1.05M
    Full specs Remove GPT-6 Luna

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

GPT-6 Luna is our pick

GPT-6 Luna is the better all-round choice, scoring 86/100 against GLM-4.7-Flash (48). It leads on capability, inputs & features and context window. GLM-4.7-Flash wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because GLM-4.7-Flash and GPT-6 Luna has no Capabilities Index score yet.

  • CapabilityGPT-6 LunaShared benchmarks: GPT-6 Luna 94.7% · GLM-4.7-Flash 35.1%
  • Lowest priceGLM-4.7-FlashGLM-4.7-Flash $0.145 · GPT-6 Luna $0.20 per 1M tokens (3:1 blend)
  • Longest contextGPT-6 LunaGPT-6 Luna 1,050,000 · GLM-4.7-Flash 200,000 tokens
  • Widest inputsGPT-6 LunaGLM-4.7-Flash: Text · GPT-6 Luna: Text, Images, PDFs
  • Self-hostingGLM-4.7-FlashPublishes downloadable weights
How the score is built
MeasureWeightGLM-4.7-FlashGPT-6 Luna
CapabilityShared benchmarks50%3595
Price25%9083
Inputs & features15%3580
Context window10%3261
Overall100%48/10086/100
02 — Side by side

Every spec in one table

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

GLM-4.7-Flash vs GPT-6 Luna specifications side by side
SpecificationGLM-4.7-FlashZ.ai (Zhipu)GPT-6 LunaOpenAI
Capability
Capabilities Index (ECI)——
ECI rank——
GPQA DiamondGraduate-level science questions45.1%90.5% (best)
FrontierMath Tiers 1–3Research-level mathematics—79.0%
OTIS Mock AIME 2024–2025Competition mathematics25.0%98.9% (best)
SimpleQA VerifiedShort factual questions—41.4%
Price per million tokens
Input$0.06 (best)$0.10
Output$0.40 (best)$0.50
Cached input—$0.01
Blended (3:1)$0.145 (best)$0.20
Long-context rateSame rateOver 272K: $0.20 / $0.75
Price sourceMedian of 13 providersOfficial OpenAI API
Limits
Context window200,000 tokens1,050,000 tokens (best)
Max output131,072 tokens (best)128,000 tokens
Inputs and features
TextYesYes
ImagesNoYes
PDFsNoYes
AudioNoNo
VideoNoNo
ReasoningYesYeslow · medium · high · xhigh · max
Tool callingYesYes
Structured outputNoYes
Availability
WeightsOpenProprietary
API model IDglm-4.7-flashgpt-6-luna
API providers1924 (best)
ReleasedJan 19, 2026Sep 22, 2026
Knowledge cutoffApr 2025May 18, 2026
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-Flash$1.41
  • GPT-6 Luna$2.00
04 — Questions

Which should you choose?

Which is better: GLM-4.7-Flash or GPT-6 Luna?

GPT-6 Luna is the better all-round choice, scoring 86/100 against GLM-4.7-Flash (48). It leads on capability, inputs & features and context window. GLM-4.7-Flash wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because GLM-4.7-Flash and GPT-6 Luna has no Capabilities Index score yet.

Which is cheaper, GLM-4.7-Flash or GPT-6 Luna?

GLM-4.7-Flash is cheaper at $0.06 input / $0.40 output per million tokens (median across 13 API providers; free on Z.AI). GPT-6 Luna costs $0.10 input / $0.50 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $0.145 per million tokens for GLM-4.7-Flash versus $0.20 for GPT-6 Luna (1.4× as much).

Which scores higher on benchmarks?

Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025): GPT-6 Luna 94.7% and GLM-4.7-Flash 35.1%. On individual benchmarks: GPQA Diamond — GPT-6 Luna 90.5%, GLM-4.7-Flash 45.1%; OTIS Mock AIME 2024–2025 — GPT-6 Luna 98.9%, GLM-4.7-Flash 25.0%.

Which is better for coding?

There are no published SWE-bench Verified results for GLM-4.7-Flash and GPT-6 Luna yet, so there is no like-for-like coding score. On overall capability, GPT-6 Luna leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Which has the bigger context window?

GPT-6 Luna has the largest context window at 1,050,000 tokens, against 200,000 for GLM-4.7-Flash. Maximum output per response: GLM-4.7-Flash up to 131,072, GPT-6 Luna up to 128,000 tokens.

Which can read images, PDFs, audio or video?

GLM-4.7-Flash accepts text; GPT-6 Luna accepts text, images and PDFs. GPT-6 Luna handles the widest range of inputs.

Are any of these open source?

GLM-4.7-Flash publishes its weights and can be self-hosted; GPT-6 Luna is proprietary.

Which is newer?

GPT-6 Luna is the newest, released Sep 22, 2026. GLM-4.7-Flash came out Jan 19, 2026. Knowledge cutoff: GLM-4.7-Flash Apr 2025, GPT-6 Luna May 18, 2026.

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

Related comparisons