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Model comparison · accepted catalogue facts 2026-10-21

Gemini 4 Argon vs GLM 5.2

A quality score or a computable price is missing. This pair has no better-and-cheaper verdict.

Editorial comparison: Cross-provider head-to-head: Gemini 4 Argon (Google) vs GLM 5.2 (Z.ai) on intelligence, speed, and pricing Inclusion does not recommend either model.

Gemini 4 Argon

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1533.2

Balanced effective USD / 1M tokens · lower is better

Unknown

Price under confirmation since Oct 21, 2026
GLM 5.2

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1470.0

Balanced effective USD / 1M tokens · lower is better

$1.12/M

Price under confirmation since Oct 12, 2026

In-Depth Analysis

Gemini 4 Argon vs GLM 5.2: Measured Technical Breakdown

Comprehensive comparison across measured human-preference evaluation, effective production token costs, architectural limits, and migration trade-offs.

Measured Capability (LMArena)
Gemini 4 Argon leads by 63.2 Elo

Gemini 4 Argon: 1533.2 ±8.8 · GLM 5.2: 1470 ±4.17 Statistically significant preference separation

Cost Comparison Across Canonical Production Workloads

Rates represent effective cost per 1M tokens under standard token blends. Cached prompt reads are factored according to workload context reuse.

Workload & RatioGemini 4 ArgonGLM 5.2Cost DifferenceAdvantage
Balanced 3 tokens in per 1 outSeparately billed reasoning tokens are not part of this token blend.$1.12 /1M tokens—Custom quote needed
Summarise Huge input, tiny outputSeparately billed reasoning tokens are not part of this token blend.$0.422 /1M tokens—Custom quote needed
Chat Long cached system promptSeparately billed reasoning tokens are not part of this token blend.$1.56 /1M tokens—Custom quote needed
Code gen Output dominatesSeparately billed reasoning tokens are not part of this token blend.$2.24 /1M tokens—Custom quote needed
Agentic Context replayed each turnSeparately billed reasoning tokens are not part of this token blend.$0.693 /1M tokens—Custom quote needed

Architectural & Operational Specifications

SpecificationGemini 4 ArgonGLM 5.2Operational Note
Context window1,048,576 tokens1,048,576 tokens—
Max output generation943,718 tokens943,718 tokens—
Base input price$4.00 / 1M tokens$0.309 / 1M tokensGLM 5.2 input rate is cheaper
Base output price$20.00 / 1M tokens$3.55 / 1M tokensGLM 5.2 output rate is cheaper
Cached input price$0.200 / 1M tokens$0.137 / 1M tokensGLM 5.2 offers lower cached prompt cost
Reasoning & thinking tokensSupported; separately billed at $20.00/1MSupported; standard rates—
Input modalitiesaudio, file, image, text, videotext—
Tool calling / function useSupportedSupported—
Open weights & licenceProprietary (API-only)Open weights (mit)GLM 5.2 allows independent hosting or local deployment
Provider & reseller marketGoogle · 6 provider offersZ.ai · 33 provider offers (9.21× spread)—

When to Choose Gemini 4 Argon

  • Higher measured general capability (1533.2 Elo on human evaluation).
  • Broader multimodal input support: audio, file, image, text, video.
  • Direct integration with Google tooling and API features.

When to Choose GLM 5.2

  • Open weights availability allows private self-hosting and on-premise execution.
  • Direct integration with Z.ai tooling and API features.
Risks when moving from Gemini 4 Argon → GLM 5.2:
  • audio input is not supported by the candidate.
  • file input is not supported by the candidate.
  • image input is not supported by the candidate.
  • video input is not supported by the candidate.

Detailed Evidence Passports & Independent Audits

Gemini 4 Argon Full model card, pricing ladder & provider offers → GLM 5.2 Full model card, pricing ladder & provider offers → Alternatives to Gemini 4 Argon Better-and-cheaper dominance audit → Alternatives to GLM 5.2 Better-and-cheaper dominance audit →

Compare · model decision

Choose two or three models

Inspect the differences, estimate your workload and keep the evidence. All standard catalogue models are available, including unrated ones.

Catalogue facts
2026-10-21

2 / 3 selected · 0 requirements
Open public comparison link
Public workload and requirements

These settings are included in the share link. The fixed-request scenario and private evaluation data below are excluded.

A ↔ B

A quality score or a computable price is missing. This pair has no better-and-cheaper verdict.

Estimated blend: 75% input, 25% output; 0% cached input. Base context tier. Prompt length selects the billing tier. An unlisted cache-read rate uses that tier’s full input rate in this blend; missing required input or output rates prevent an estimate. The fixed-request estimate below validates actual context and output limits and requires a published cache-read rate when cache hits are assumed.

What changes between these models

Quality first, then estimated price. LMArena scores are human-preference measurements, not a benchmark of your application. A missing score is unrated.

  • Gemini 4 Argon · Meets the stated model requirements
  • GLM 5.2 · Meets the stated model requirements
Material differences and unknown evidence. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA Gemini 4 ArgonB GLM 5.2
LMArena general score (Elo)1,533.21,470Δ vs A: -63.2 Elo
Score interval half-width (± Elo)8.84.17Δ vs A: -4.63 Elo
Benchmark reasoning efforthighmax
LMArena document score (Elo)UnknownUnknown
Estimated effective USD / 1M tokensUnknown$1.12
Base input USD / 1M tokens$4$0.309Δ vs A: -3.691 USD/M
Base output USD / 1M tokens$20$3.553Δ vs A: -16.447 USD/M
Base cached-input USD / 1M tokens$0.2$0.137Δ vs A: -0.063 USD/M
Price statusUnder confirmation since 2026-10-21Under confirmation since 2026-10-12
Input modalitiesaudio, file, image, text, videotext
Open weightsNot supportedSupportedΔ vs A: +1
Recorded licenceUnknownmit
Model providerGoogleZ.ai
Distinct sellers in the catalogue126Δ vs A: +25
Serving precision differs across offersNot supportedSupportedΔ vs A: +1
Recorded retirement dateUnknownUnknown
5 shared measured facts
Shared measured facts. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA Gemini 4 ArgonB GLM 5.2
Context window (tokens)1,048,5761,048,576Δ vs A: 0 tokens
Output limit (tokens)943,718943,718Δ vs A: 0 tokens
Tool useSupportedSupportedΔ vs A: 0
ReasoningSupportedSupportedΔ vs A: 0
Retirement evidenceNone announced; not a guaranteeNone announced; not a guarantee

Provider counts and precision flags describe catalogue offers. They do not establish the precision, availability, region, tool behaviour or limits of your chosen endpoint. Open each model’s provider table before switching.

Complete price ladders and billing conditions

Gemini 4 Argon

Estimate unavailable: Separately billed reasoning tokens are not part of this token blend.

Separate reasoning: $20/1M tokens; reasoning-token counts are not modelled here. Rates below are USD per 1M tokens. A threshold selects that whole input/output rate pair.

Input-token thresholdInputOutputCached input
Base, up to and including first threshold$4$20$0.2

GLM 5.2

Rates below are USD per 1M tokens. A threshold selects that whole input/output rate pair.

Input-token thresholdInputOutputCached input
Base, up to and including first threshold$0.309$3.553$0.137
Migration preflight · compare each candidate with model A

Keeping Gemini 4 Argon is a valid decision. No pairwise comparison certifies a drop-in replacement.

Gemini 4 Argon → GLM 5.2

Known loss or failed requirement

  • audio input is not supported by the candidate.
  • file input is not supported by the candidate.
  • image input is not supported by the candidate.
  • video input is not supported by the candidate.
  • Provider endpoint, serving precision, availability and task success need your own verification. Catalogue model capabilities do not certify an endpoint.
Check alternatives to Gemini 4 Argon

Estimate a fixed request shape

Private to this tab unless you save locally or explicitly export private details. These are estimates, not observed invoices. Cross-model token counts and task success are assumptions.

0.1 extra attempts means 10 extra full attempts per 100 requests. Cache writes, tool charges, taxes and unlisted charges are excluded.

  • Gemini 4 ArgonEstimated total: unknown Separately billed reasoning tokens are not part of this request shape.
  • GLM 5.2Estimated total: $2.3945 1,000 full attempts · base rates
Prompt and output sensitivity · sampled scenarios

Every point recalculates the entire request shape. Tier-boundary samples include one token below, at and above each threshold. Ordering between samples is not guaranteed. Lowest known cost excludes unknown estimates and is not a replacement recommendation.

Vary input; hold output, requests, cache and retries fixed.
Input tokensGemini 4 ArgonGLM 5.2Coverage
2,000unknownSeparately billed reasoning tokens are not part of this request shape.$2.3945Lowest knownIncomplete
4,096unknownSeparately billed reasoning tokens are not part of this request shape.$3.042164Lowest knownIncomplete
32,768unknownSeparately billed reasoning tokens are not part of this request shape.$11.901812Lowest knownIncomplete
131,072unknownSeparately billed reasoning tokens are not part of this request shape.$42.277748Lowest knownIncomplete
Vary output; hold input, requests, cache and retries fixed.
Output tokensGemini 4 ArgonGLM 5.2
128unknownSeparately billed reasoning tokens are not part of this request shape.$1.072784
2,048unknownSeparately billed reasoning tokens are not part of this request shape.$7.894544
8,192unknownSeparately billed reasoning tokens are not part of this request shape.$29.724176

Bring your observed evaluation results

JSON is parsed locally. Prompts, filenames, individual responses and extra fields are not retained. Import only aggregate measurements for selected models. Task/version labels do not establish that two evaluations used the same dataset.

Expected format
[{"model":"provider/exact-model-id","task":"support-answer","version":"rubric-1","attempts":100,"successes":84,"costUsd":2.4,"latencyMs":950}]

Use the exact catalogue ID. Replace the example values with observed measurements. Omit costUsd or latencyMs when they are unknown. No public score is inferred from these observations.

Keep the decision and its evidence

A portable record retains selected facts, complete tier ladders, public requirements and the publication manifest. It also embeds the full permitted public catalogue to verify the input artifact checksum. Files are larger for this reason (up to 2 MB); local storage is limited to 12 records. Team review is local to this browser; there is no hosted collaboration.

Saving is opt-in and includes private review fields. Shared comparison links never include them. Version 1 Check references remain separate and are not overwritten or guessed into this format.

Saved local decisions · 0

No version 2 decision records are stored in this browser.

Questions this page answers

Is Gemini 4 Argon a better-and-cheaper replacement for GLM 5.2?

A quality score or a computable price is missing. This pair has no better-and-cheaper verdict.

Which model is higher rated: Gemini 4 Argon or GLM 5.2?

Gemini 4 Argon scores higher on LMArena general (1533.2 ±8.8) compared to GLM 5.2 (1470.0 ±4.2), a margin of 63.2 Elo points. The measured confidence intervals do not overlap, demonstrating a statistically significant preference lead on human evaluations.

Which model is more cost-effective: Gemini 4 Argon or GLM 5.2?

Gemini 4 Argon bills reasoning tokens separately. Direct pricing comparison requires evaluating your specific input/output prompt ratios and reasoning settings.

How do their context windows and generation limits compare?

Both Gemini 4 Argon and GLM 5.2 provide an identical 1,048,576-token context window. Maximum output generation limits are 943,718 tokens for Gemini 4 Argon and 943,718 tokens for GLM 5.2.

What capabilities are lost when switching between Gemini 4 Argon and GLM 5.2?

Switching from Gemini 4 Argon to GLM 5.2 involves: audio input is not supported by the candidate; file input is not supported by the candidate; image input is not supported by the candidate; video input is not supported by the candidate. Run test suites on your specific prompt templates and tool declarations before cutting over production traffic.

Can Gemini 4 Argon or GLM 5.2 be self-hosted or deployed privately?

GLM 5.2 provides open weights, enabling self-hosting on private GPU infrastructure, local quantization, and private data pipelines. Managed inference is also offered across third-party API providers (6 for Gemini 4 Argon, 33 for GLM 5.2).

What is included in this comparison?

Accepted model capabilities, context and output limits, complete price-tier ladders, independent LMArena measurements and missing evidence. Provider endpoint precision and application success still need verification.

Can I compare a different workload or keep a decision record?

Yes. Change the public workload, prompt length and requirements in the comparison workspace. Fixed-request scenarios, aggregate evaluations and review notes stay local unless explicitly exported.

Evidence & Ask
Markdown for LLMs