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Model comparison

Gemini 3.1 Pro vs GLM 5.3

Google against Z.ai, compared on context, price, and attributed benchmark results.

Recorded profile verification October 2, 2026Individual provider fields may changeEvidence confidence: Medium — see receipts below

Google

Gemini 3.1 Pro

Frontier

vs

Z.ai

GLM 5.3

Frontier · Open weights

AI model capability comparison
SpecificationGemini 3.1 ProGLM 5.3
ProviderGoogleZ.ai
TierFrontierFrontier
Context windowWinner: 1.05M1M
Max output66KWinner: 131K
Input / 1M tokens$2Winner: $1.40
Output / 1M tokens$12Winner: $4.40
WeightsClosedOpen
ParametersNot verifiedUnverified753B total (MoE; active count not verified)
Reasoning levelslow, medium, highlow, high, max
Modalitiestext, image, video, audio, pdftext
LicenseNot verifiedUnverifiedGLM-5.3 License (custom)
API model idgemini-3.1-pro-previewglm-5.3
ReleasedFebruary 19, 2026August 14, 2026
SWE-bench Verified · source date Gemini 3.1 Pro: 2026-0280.6Not verifiedUnverified
GPQA Diamond · source date Gemini 3.1 Pro: 2026-0294.3Not verifiedUnverified
Humanity's Last Exam · source date Gemini 3.1 Pro: 2026-02, GLM 5.3: 2026-08-14 (comparability not verified)44.462.5
Terminal-Bench 2.1 · source date Gemini 3.1 Pro: 2026-07, GLM 5.3: 2026-08-14 (comparability not verified)73.888.2
Artificial Analysis Intelligence Index [max] · source date GLM 5.3: 2026-10-01Not verifiedUnverified45
DeepSWE 1.1 · source date GLM 5.3: 2026-08-14Not verifiedUnverified66.9

Dates beside benchmark scores refer to the linked source’s publication or capture date unless that source explicitly states when it measured the result. A shared URL alone does not establish comparable runs; unverified setups are marked. Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

Where each score comes from, and how far it can be compared across models.

Pricing tiers

Gemini 3.1 Pro: The current Gemini developer guide lists $2/$12 per MTok below 200K input and $4/$18 above 200K, including thinking output. It leaves the exact 200K boundary unspecified; confirm that boundary and other processing/cache tiers before budgeting. The exact API ID remains gemini-3.1-pro-preview. Restricted Gemini 4 Argon is a separate preview and does not make this endpoint stable.

GLM 5.3: Z.ai token API $1.40/$4.40 per MTok; cached input $0.26. Cache storage is listed as limited-time free, with no verified end date. Coding Plan points are separate: off-peak/weekend calls consume half the standard points. API docs advertise 1M context and specify a 131,072 output ceiling.

FrontierRecord checked October 1, 2026

Gemini 3.1 Pro

Gemini 3.1 Pro Preview is Google's multimodal Pro endpoint with a 1,048,576-token input limit and 65,536-token output limit.

Best for

  • Very long documents
  • Multimodal reasoning
  • Frontier knowledge work

Watch out

This API endpoint remains Preview. A separate customtools endpoint is optimized for bash/custom-tool workflows and can behave differently on other tasks. Keep endpoint, thinking level and benchmark version matched; restricted Gemini 4 Argon access is a separate lifecycle.

FrontierOpen weightsRecord checked October 2, 2026

GLM 5.3

Z.ai's text-only GLM flagship, with a 753B-total MoE checkpoint and always-enabled reasoning. Published benchmark figures identify particular harnesses and settings.

Best for

  • Coding Plan subscribers
  • Long-horizon coding
  • Chinese + English

Watch out

Custom licence: model-as-a-service operators whose licensee/affiliate aggregate revenue exceeds $10B in any consecutive 12 months require Z.AI security review before commercial use; the definition excludes embedded end-user features/harnesses and mere relays. Keep attribution/permission notices. Reasoning defaults to max; disabling it fails. Accounts that ever subscribed to Coding Plan, including expired subscriptions, currently have Model API access only through the Chat Completions-compatible protocol. Active parameter count and exact initial weight-publication day remain unverified.

When the cheaper one wins

GLM 5.3 is cheaper on output at $4.40 per million tokens against $12 for Gemini 3.1 Pro — about 2.7×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. On DeepSWE 1.1, GLM 5.3 is 69% Pass@1 at $3.99/task versus Gemini 3.1 Pro at 11.7% / $2.14/task. This compares standard short-context token rates, not total cost per accepted task. Include input, cache, context tiers, schedules, retries and review in your pilot.

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Evidence confidence: Medium

How strong and complete the evidence behind this comparison is — not a prediction of which model is better.

  • Pricing verified on both sides — Input and output rates are verified for both models.
  • 4/5 core specs verified on both sides — Not published for at least one side: parameter count.
  • No directly aligned benchmark setup — No benchmark has matching cited sources, source dates, units, effort and recorded run details on both models.
  • Verified within the last 90 days — Older of the two catalog checks was 25 days ago.
  • Both models carry source citations — Each side has at least two catalog sources on record.

Source receipts

Each catalog figure was checked against the provider or an independent second source on the date shown.

Related comparisons

Diving deeper on one model? Gemini 3.1 Pro · GLM 5.3

Common questions

Gemini 3.1 Pro vs GLM 5.3

Answered from the verified figures on this page rather than general guidance.

Is Gemini 3.1 Pro or GLM 5.3 cheaper for input?
GLM 5.3 is cheaper at $1.40 per million input tokens, against $2 for Gemini 3.1 Pro — roughly 1.4× the price. Estimate both input and output token volume for your workload. These are base rates; Gemini 3.1 Pro: The current Gemini developer guide lists $2/$12 per MTok below 200K input and $4/$18 above 200K, including thinking output. It leaves the exact 200K boundary unspecified; confirm that boundary and other processing/cache tiers before budgeting. The exact API ID remains gemini-3.1-pro-preview. Restricted Gemini 4 Argon is a separate preview and does not make this endpoint stable. GLM 5.3: Z.ai token API $1.40/$4.40 per MTok; cached input $0.26. Cache storage is listed as limited-time free, with no verified end date. Coding Plan points are separate: off-peak/weekend calls consume half the standard points. API docs advertise 1M context and specify a 131,072 output ceiling.
Is Gemini 3.1 Pro or GLM 5.3 cheaper for output?
GLM 5.3 is cheaper at $4.40 per million output tokens, against $12 for Gemini 3.1 Pro — roughly 2.7× the price. Estimate both input and output token volume for your workload. These are base rates; Gemini 3.1 Pro: The current Gemini developer guide lists $2/$12 per MTok below 200K input and $4/$18 above 200K, including thinking output. It leaves the exact 200K boundary unspecified; confirm that boundary and other processing/cache tiers before budgeting. The exact API ID remains gemini-3.1-pro-preview. Restricted Gemini 4 Argon is a separate preview and does not make this endpoint stable. GLM 5.3: Z.ai token API $1.40/$4.40 per MTok; cached input $0.26. Cache storage is listed as limited-time free, with no verified end date. Coding Plan points are separate: off-peak/weekend calls consume half the standard points. API docs advertise 1M context and specify a 131,072 output ceiling.
Which has the larger context window, Gemini 3.1 Pro or GLM 5.3?
Gemini 3.1 Pro accepts 1.05M tokens against 1M for GLM 5.3. This only matters if you routinely send very long documents or large codebases.
Do Gemini 3.1 Pro and GLM 5.3 support the same reasoning levels?
Gemini 3.1 Pro exposes low, medium, high, while GLM 5.3 exposes low, high, max.
Should I use Gemini 3.1 Pro or GLM 5.3?
Both are classified in our frontier tier; this editorial grouping does not imply equal capability. Gemini 3.1 Pro is a candidate for very long documents; GLM 5.3 is a candidate for coding plan subscribers. Compare accuracy, tool reliability, latency and cost on representative tasks.
Can I self-host Gemini 3.1 Pro or GLM 5.3?
GLM 5.3 publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. Gemini 3.1 Pro is a closed model whose supported access paths are controlled by its provider.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

  • Input price: GLM 5.3 costs $1.40 per 1M tokens versus $2 for Gemini 3.1 Pro — a 1.4x difference at the headline tier.
  • Context: Gemini 3.1 Pro takes 1.05M against 1M for GLM 5.3 — only decisive if your prompts approach the smaller window.
  • Deployment: GLM 5.3 publishes weights you can self-host; the other is API-only.
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