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Model comparison

GLM-4.7 vs GPT-5.4 nano

Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

GLM-4.7

Z.AI

60.4/100

Supported · Public rank #46

90% interval 46.7–74.0

GPT-5.4 nano

OpenAI

66.0/100

Supported · Public rank #28

90% interval 55.5–76.4

GPT-5.4 nano has the higher public score estimate, 65.98 versus 60.39, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

5 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Long documents

    Prompts that approach the documented context limit

    GPT-5.4 nano

    GPT-5.4 nano has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
5
GLM-4.7 only
8
GPT-5.4 nano only
8
Like-for-like categories
1 / 8

2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Math

Like-for-like
GLM-4.7
1.8
GPT-5.4 nano
21.0
Weighted basis
2 vs 2 rows
Reading
GPT-5.4 nano leads

Agentic

Directional only
GLM-4.7
45.7
GPT-5.4 nano
42.9
Weighted basis
2 vs 2 rows
Reading
Directional only

Knowledge

Directional only
GLM-4.7
51.8
GPT-5.4 nano
43.8
Weighted basis
3 vs 2 rows
Reading
Directional only

Coding

Not comparable
GLM-4.7
75.4
GPT-5.4 nano
Not measured
Weighted basis
3 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
GLM-4.7
Not measured
GPT-5.4 nano
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GLM-4.7
Not measured
GPT-5.4 nano
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GLM-4.7
Not measured
GPT-5.4 nano
66.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
GLM-4.7
Not measured
GPT-5.4 nano
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

GLM-4.7
Self-hosted; infrastructure cost varies
Fits in one request
GPT-5.4 nano
$0.00082
Fits in one request

GLM-4.7 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-4.7
Self-hosted; infrastructure cost varies
Fits in one request
GPT-5.4 nano
$0.01375
Fits in one request

GLM-4.7 has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GLM-4.7
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
GPT-5.4 nano
$0.0205
Fits in one request

GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GLM-4.7

No comparable hosted API rate

GPT-5.4 nano

$0.02 per 1M cached input tokens

OpenAI pricing

Provider availability

GLM-4.7

Not sourced

GPT-5.4 nano

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

GLM-4.7

Reasoning

GPT-5.4 nano

Reasoning

Weight access

GLM-4.7

Open Weight

GPT-5.4 nano

Proprietary

License

GLM-4.7

Open Weight

GPT-5.4 nano

Proprietary

Release date

GLM-4.7

2025-10-01

GPT-5.4 nano

2026-03-17

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
GPT-5.4 nano has the higher public score estimate, 65.98 versus 60.39, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
GPT-5.4 nano has the larger documented window (400K).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence21 rows

Agentic

  • Terminal-Bench 2.0

    GLM-4.741%
    Source
    GPT-5.4 nano46.3%
    Source

    GPT-5.4 nano leads this result

  • BrowseComp

    GLM-4.752%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • VITA-Bench

    GLM-4.715.5%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • Gert Labs

    GLM-4.739.95%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • OSWorld-Verified

    GLM-4.7—
    GPT-5.4 nano39%
    Source

    Not directly comparable

  • MCP Atlas

    GLM-4.7—
    GPT-5.4 nano56.1%
    Source

    Not directly comparable

  • Toolathlon

    GLM-4.7—
    GPT-5.4 nano35.5%
    Source

    Not directly comparable

  • τ²-bench results

    GLM-4.7—
    GPT-5.4 nano92.5%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    GLM-4.773.8%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • LiveCodeBench

    GLM-4.784.9%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • SWE-Rebench

    GLM-4.758.7%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • Vibe Code Bench

    GLM-4.7—
    GPT-5.4 nano26.10%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GLM-4.785.7%
    Source
    GPT-5.4 nano82.8%
    Source

    GLM-4.7 leads this result

  • MMLU-Pro

    GLM-4.784.3%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • HLE

    GLM-4.724.8%
    Source
    GPT-5.4 nano37.7%
    Source

    GPT-5.4 nano leads this result

  • HLE w/o tools

    GLM-4.7—
    GPT-5.4 nano24.3%
    Source

    Not directly comparable

Math

  • AIME 2025

    GLM-4.795.7%
    Source
    GPT-5.4 nano—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    GLM-4.72.439%
    GPT-5.4 nano25.860%

    GPT-5.4 nano leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    GLM-4.70.000%
    GPT-5.4 nano6.250%

    GPT-5.4 nano leads this result

Multimodal

  • MMMU-Pro

    GLM-4.7—
    GPT-5.4 nano66.1%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    GLM-4.7—
    GPT-5.4 nano69.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GLM-4.7 or GPT-5.4 nano?

GPT-5.4 nano has the higher public score estimate, 65.98 versus 60.39, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GLM-4.7 or GPT-5.4 nano?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, GLM-4.7 or GPT-5.4 nano?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GLM-4.7 or GPT-5.4 nano?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GLM-4.7 or GPT-5.4 nano?

GPT-5.4 nano has the larger documented context window: 400K, compared with 200K.

Related comparisons

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Last updated July 30, 2026

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