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Model comparison

Gemini 3.1 Pro vs GLM-5-Turbo

Updated July 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Gemini 3.1 Pro

Google

54.7/100

Estimated · Public rank #87

90% interval 38.8–70.6

GLM-5-Turbo

Z.AI

65.9/100

Supported · Public rank #29

90% interval 56.1–75.7

GLM-5-Turbo has the higher public score estimate, 65.92 versus 54.66, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 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

    Gemini 3.1 Pro

    Gemini 3.1 Pro has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GLM-5-Turbo

    GLM-5-Turbo has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    GLM-5-Turbo

    GLM-5-Turbo has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • 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

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • 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-5-Turbo does not fit this workload in one request. GLM-5-Turbo has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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
1
Gemini 3.1 Pro only
22
GLM-5-Turbo only
0
Like-for-like categories
0 / 8

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.

Agentic

Not comparable
Gemini 3.1 Pro
Not measured
GLM-5-Turbo
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Gemini 3.1 Pro
Not measured
GLM-5-Turbo
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 3.1 Pro
77.1
GLM-5-Turbo
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 3.1 Pro
Not measured
GLM-5-Turbo
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Gemini 3.1 Pro
31.8
GLM-5-Turbo
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.1 Pro
Not measured
GLM-5-Turbo
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.1 Pro
82.6
GLM-5-Turbo
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.1 Pro
Not measured
GLM-5-Turbo
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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

Gemini 3.1 Pro
$0.008
Fits in one request
GLM-5-Turbo
$0.0032
Fits in one request

GLM-5-Turbo has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.1 Pro
$0.136
Fits in one request
GLM-5-Turbo
$0.072
Fits in one request

GLM-5-Turbo has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Gemini 3.1 Pro
$0.2
Fits in one request
GLM-5-Turbo
$0.304
Does not fit in one request
Cached input priced at the published list-input rate

GLM-5-Turbo does not fit this workload in one request. GLM-5-Turbo has no published cached-input rate, so cached tokens use its listed input 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.

Gemini 3.1 Pro

$0.2 per 1M cached input tokens

Google Gemini API pricing

GLM-5-Turbo

Not published

Reasoning profile

Gemini 3.1 Pro

Reasoning

GLM-5-Turbo

Reasoning

Weight access

Gemini 3.1 Pro

Proprietary

GLM-5-Turbo

Proprietary

License

Gemini 3.1 Pro

Proprietary

GLM-5-Turbo

Proprietary

Release date

Gemini 3.1 Pro

2026-02-19

GLM-5-Turbo

2026-03-01

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
GLM-5-Turbo has the higher public score estimate, 65.92 versus 54.66, but the 90% score intervals overlap.
Workload cost
Repository review: $0.136 vs $0.072. Cache-heavy agent loop: $0.2 vs $0.304.
Context tradeoff
Gemini 3.1 Pro has the larger documented window (1M).

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 evidence23 rows

Agentic

  • Claw-Eval

    Shared source
    Gemini 3.1 Pro57.8%
    GLM-5-Turbo55.8%

    Gemini 3.1 Pro leads this result

  • DeepSearchQA

    Gemini 3.1 Pro69.7%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • τ²-bench results

    Gemini 3.1 Pro95.6%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • Gert Labs

    Gemini 3.1 Pro56.87%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • ResearchClawBench

    Gemini 3.1 Pro13.3%
    Source
    GLM-5-Turbo—

    Not directly comparable

Coding

  • LiveCodeBench Pro

    Gemini 3.1 Pro82.9%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • React Native Evals

    Gemini 3.1 Pro78.9%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • Vibe Code Bench

    Gemini 3.1 Pro32.03%
    Source
    GLM-5-Turbo—

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Gemini 3.1 Pro77.1%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • ARC-AGI-3

    Gemini 3.1 Pro0.4%
    Source
    GLM-5-Turbo—

    Not directly comparable

Knowledge

  • GPQA-D

    Gemini 3.1 Pro94.3%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • HLE w/o tools

    Gemini 3.1 Pro45.4%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • HealthBench Hard

    Gemini 3.1 Pro20.6%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • MedXpertQA (Text)

    Gemini 3.1 Pro71.5%
    Source
    GLM-5-Turbo—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 3.1 Pro36.900%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 3.1 Pro16.700%
    Source
    GLM-5-Turbo—

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemini 3.1 Pro83.9%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • CharXiv

    Gemini 3.1 Pro80.2%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • ERQA

    Gemini 3.1 Pro69.4%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • SimpleVQA

    Gemini 3.1 Pro72.4%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • ScreenSpot Pro

    Gemini 3.1 Pro84.4%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • ZeroBench

    Gemini 3.1 Pro29.0%
    Source
    GLM-5-Turbo—

    Not directly comparable

  • MedXpertQA (MM)

    Gemini 3.1 Pro81.3%
    Source
    GLM-5-Turbo—

    Not directly comparable

Frequently asked questions

Which is better, Gemini 3.1 Pro or GLM-5-Turbo?

GLM-5-Turbo has the higher public score estimate, 65.92 versus 54.66, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Gemini 3.1 Pro or GLM-5-Turbo?

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, Gemini 3.1 Pro or GLM-5-Turbo?

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

Which costs less, Gemini 3.1 Pro or GLM-5-Turbo?

For the stated presets, chat costs $0.008 on Gemini 3.1 Pro and $0.0032 on GLM-5-Turbo; repository review costs $0.136 and $0.072; the cache-heavy agent loop costs $0.2 and $0.304. GLM-5-Turbo does not fit this workload in one request. GLM-5-Turbo has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Gemini 3.1 Pro or GLM-5-Turbo?

Gemini 3.1 Pro has the larger documented context window: 1M, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated July 29, 2026

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