Undominated.ai

Model comparison · accepted catalogue facts 2026-09-23

MiniMax M3 vs GLM 5.3

Neither model has both an equal-or-higher measured score and an equal-or-lower estimated price in this pair. Retaining your current model is a valid outcome.

Editorial comparison: Cross-provider agent shortlist: tool support alone does not establish equivalent task success. Inclusion does not recommend either model.

MiniMax M3

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1433.5

Balanced effective USD / 1M tokens · lower is better

$0.525/M

GLM 5.3

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1475.1

Balanced effective USD / 1M tokens · lower is better

$1.29/M

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
2026-09-23

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

Neither model has both an equal-or-higher measured score and an equal-or-lower estimated price in this pair. Retaining your current model is a valid outcome.

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.

  • MiniMax M3 · Meets the stated model requirements
  • GLM 5.3 · Meets the stated model requirements
Material differences and unknown evidence. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA MiniMax M3B GLM 5.3
LMArena general score (Elo)1,433.51,475.1Δ vs A: +41.6 Elo
Score interval half-width (± Elo)4.196.4Δ vs A: +2.21 Elo
Benchmark reasoning effortdefaultmax
LMArena document score (Elo)1,434.8Unknown
Estimated effective USD / 1M tokens$0.525$1.29Δ vs A: +0.765 USD/M
Base input USD / 1M tokens$0.3$0.84Δ vs A: +0.54 USD/M
Base output USD / 1M tokens$1.2$2.64Δ vs A: +1.44 USD/M
Base cached-input USD / 1M tokens$0.06$0.156Δ vs A: +0.096 USD/M
Context window (tokens)1,048,5761,310,720Δ vs A: +262,144 tokens
Output limit (tokens)512,000131,072Δ vs A: -380,928 tokens
Input modalitiestext, image, videotext
Open weightsSupportedNot supportedΔ vs A: -1
Recorded licenceminimax-communityproprietary
Model providerMiniMaxZ.ai
Distinct sellers in the catalogue1331Δ vs A: +18
Recorded retirement dateUnknownUnknown
4 shared measured facts
Shared measured facts. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA MiniMax M3B GLM 5.3
Tool useSupportedSupportedΔ vs A: 0
ReasoningSupportedSupportedΔ vs A: 0
Serving precision differs across offersSupportedSupportedΔ 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

MiniMax M3

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$0.3$1.2$0.06

GLM 5.3

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$0.84$2.64$0.156
Migration preflight · compare each candidate with model A

Keeping MiniMax M3 is a valid decision. No pairwise comparison certifies a drop-in replacement.

MiniMax M3 → GLM 5.3

Known loss or failed requirement

  • Output limit: 512,000 → 131,072 tokens.
  • 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 MiniMax M3

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.

  • MiniMax M3Estimated total: $1.2 1,000 full attempts · base rates
  • GLM 5.3Estimated total: $3 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 tokensMiniMax M3GLM 5.3Coverage
2,000$1.2Lowest known$3All computed
4,096$1.8288Lowest known$4.76064All computed
32,768$10.4304Lowest known$28.84512All computed
131,072$39.9216Lowest known$111.42048All computed
Vary output; hold input, requests, cache and retries fixed.
Output tokensMiniMax M3GLM 5.3
128$0.7536$2.01792
2,048$3.0576$7.08672
8,192$10.4304$23.30688

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 MiniMax M3 a better-and-cheaper replacement for GLM 5.3?

Neither model has both an equal-or-higher measured score and an equal-or-lower estimated price in this pair. Retaining your current model is a valid outcome.

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