Undominated.ai

Model comparison · accepted catalogue facts 2026-10-21

Claude Sonnet 4.6 vs GLM 5.2

GLM 5.2 has an equal-or-higher measured score and an equal-or-lower estimated price. Switching from Claude Sonnet 4.6 loses capability or fails a requirement. Review the migration checks. The reported score intervals do not overlap; this is not a task-specific success guarantee.

Editorial comparison: Cross-provider head-to-head: GLM 5.2 (Z.ai) vs Claude Sonnet 4.6 (Anthropic) on intelligence, speed, and pricing Inclusion does not recommend either model.

Claude Sonnet 4.6

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1457.8

Balanced effective USD / 1M tokens · lower is better

$6.00/M

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

Claude Sonnet 4.6 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)
GLM 5.2 leads by 12.2 Elo

Claude Sonnet 4.6: 1457.8 ±3.5 · 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 & RatioClaude Sonnet 4.6GLM 5.2Cost DifferenceAdvantage
Balanced 3 tokens in per 1 out$6.00 /1M tokens$1.12 /1M tokens$4.88 (81% diff)GLM 5.2 costs less
Summarise Huge input, tiny output$2.83 /1M tokens$0.422 /1M tokens$2.41 (85% diff)GLM 5.2 costs less
Chat Long cached system prompt$6.99 /1M tokens$1.56 /1M tokens$5.44 (78% diff)GLM 5.2 costs less
Code gen Output dominates$9.98 /1M tokens$2.24 /1M tokens$7.74 (78% diff)GLM 5.2 costs less
Agentic Context replayed each turn$3.19 /1M tokens$0.693 /1M tokens$2.50 (78% diff)GLM 5.2 costs less

Architectural & Operational Specifications

SpecificationClaude Sonnet 4.6GLM 5.2Operational Note
Context window1,000,000 tokens1,048,576 tokens—
Max output generation128,000 tokens943,718 tokensGLM 5.2 allows longer single-turn completions
Base input price$3.00 / 1M tokens$0.309 / 1M tokensGLM 5.2 input rate is cheaper
Base output price$15.00 / 1M tokens$3.55 / 1M tokensGLM 5.2 output rate is cheaper
Cached input price$0.300 / 1M tokens$0.137 / 1M tokensGLM 5.2 offers lower cached prompt cost
Reasoning & thinking tokensSupported; standard ratesSupported; standard rates—
Input modalitiestext, image, filetext—
Tool calling / function useSupportedSupported—
Open weights & licenceProprietary (proprietary)Open weights (mit)GLM 5.2 allows independent hosting or local deployment
Provider & reseller marketAnthropic · 9 provider offersZ.ai · 33 provider offers (9.21× spread)—

When to Choose Claude Sonnet 4.6

  • Broader multimodal input support: text, image, file.
  • Direct integration with Anthropic tooling and API features.
Risks when moving from GLM 5.2 → Claude Sonnet 4.6:
  • Context window: 1,048,576 → 1,000,000 tokens.
  • Output limit: 943,718 → 128,000 tokens.

When to Choose GLM 5.2

  • Higher measured general capability (1470 Elo on human evaluation).
  • Larger context window (1,048,576 tokens) for long-document or repo-level context.
  • Higher maximum completion limit (943,718 tokens).
  • Open weights availability allows private self-hosting and on-premise execution.
  • Direct integration with Z.ai tooling and API features.
Risks when moving from Claude Sonnet 4.6 → GLM 5.2:
  • image input is not supported by the candidate.
  • file input is not supported by the candidate.

Detailed Evidence Passports & Independent Audits

Claude Sonnet 4.6 Full model card, pricing ladder & provider offers → GLM 5.2 Full model card, pricing ladder & provider offers → Alternatives to Claude Sonnet 4.6 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

GLM 5.2 has an equal-or-higher measured score and an equal-or-lower estimated price. Switching from Claude Sonnet 4.6 loses capability or fails a requirement. Review the migration checks. The reported score intervals do not overlap; this is not a task-specific success guarantee.

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.

  • Claude Sonnet 4.6 · 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 Claude Sonnet 4.6B GLM 5.2
LMArena general score (Elo)1,457.81,470Δ vs A: +12.2 Elo
Score interval half-width (± Elo)3.54.17Δ vs A: +0.67 Elo
Benchmark reasoning effortdefaultmax
LMArena document score (Elo)1,482.2Unknown
Estimated effective USD / 1M tokens$6$1.12Δ vs A: -4.88 USD/M
Base input USD / 1M tokens$3$0.309Δ vs A: -2.691 USD/M
Base output USD / 1M tokens$15$3.553Δ vs A: -11.447 USD/M
Base cached-input USD / 1M tokens$0.3$0.137Δ vs A: -0.163 USD/M
Price statusAs of the catalogue dateUnder confirmation since 2026-10-12
Context window (tokens)1,000,0001,048,576Δ vs A: +48,576 tokens
Output limit (tokens)128,000943,718Δ vs A: +815,718 tokens
Input modalitiestext, image, filetext
Open weightsNot supportedSupportedΔ vs A: +1
Recorded licenceproprietarymit
Model providerAnthropicZ.ai
Distinct sellers in the catalogue526Δ vs A: +21
Serving precision differs across offersNot supportedSupportedΔ vs A: +1
Recorded retirement dateUnknownUnknown
3 shared measured facts
Shared measured facts. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA Claude Sonnet 4.6B GLM 5.2
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

Claude Sonnet 4.6

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$3$15$0.3

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 Claude Sonnet 4.6 is a valid decision. No pairwise comparison certifies a drop-in replacement.

Claude Sonnet 4.6 → GLM 5.2

Known loss or failed requirement

  • image input is not supported by the candidate.
  • file 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 Claude Sonnet 4.6

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.

  • Claude Sonnet 4.6Estimated total: $13.5 1,000 full attempts · base rates
  • 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 tokensClaude Sonnet 4.6GLM 5.2Coverage
2,000$13.5$2.3945Lowest knownAll computed
4,096$19.788$3.042164Lowest knownAll computed
32,768$105.804$11.901812Lowest knownAll computed
131,072$400.716$42.277748Lowest knownAll computed
Vary output; hold input, requests, cache and retries fixed.
Output tokensClaude Sonnet 4.6GLM 5.2
128$7.92$1.072784
2,048$36.72$7.894544
8,192$128.88$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 Claude Sonnet 4.6 a better-and-cheaper replacement for GLM 5.2?

GLM 5.2 has an equal-or-higher measured score and an equal-or-lower estimated price. Switching from Claude Sonnet 4.6 loses capability or fails a requirement. Review the migration checks. The reported score intervals do not overlap; this is not a task-specific success guarantee.

Which model is higher rated: Claude Sonnet 4.6 or GLM 5.2?

GLM 5.2 scores higher on LMArena general (1470.0 ±4.2) compared to Claude Sonnet 4.6 (1457.8 ±3.5), a margin of 12.2 Elo points. The measured confidence intervals do not overlap, demonstrating a statistically significant preference lead on human evaluations.

Which model is more cost-effective: Claude Sonnet 4.6 or GLM 5.2?

On a standard balanced workload (3 prompt tokens per 1 generated token), GLM 5.2 costs $1.12 (price under confirmation since 2026-10-12) per 1M tokens versus $6.00 for Claude Sonnet 4.6. Claude Sonnet 4.6 is approximately 5.4× the price of GLM 5.2. Base input rates are $3.00 for Claude Sonnet 4.6 and $0.309 for GLM 5.2; base output rates are $15.00 for Claude Sonnet 4.6 and $3.55 for GLM 5.2.

How do their context windows and generation limits compare?

Claude Sonnet 4.6 provides a 1,000,000-token context window with a maximum output limit of 128,000 tokens. GLM 5.2 provides a 1,048,576-token context window with a maximum output of 943,718 tokens.

What capabilities are lost when switching between Claude Sonnet 4.6 and GLM 5.2?

Switching from Claude Sonnet 4.6 to GLM 5.2 involves: image input is not supported by the candidate; file input is not supported by the candidate. Conversely, moving from GLM 5.2 to Claude Sonnet 4.6 involves: Context window: 1,048,576 → 1,000,000 tokens; Output limit: 943,718 → 128,000 tokens. Run test suites on your specific prompt templates and tool declarations before cutting over production traffic.

Can Claude Sonnet 4.6 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 (9 for Claude Sonnet 4.6, 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