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Model comparison · accepted catalogue facts 2026-10-21

MiMo-V2.5-Pro vs GLM 5.1

MiMo-V2.5-Pro has an equal-or-higher measured score and an equal-or-lower estimated price. No known model-capability loss from GLM 5.1; endpoint and task checks still apply. The reported score intervals overlap; this is not an established quality improvement.

Editorial comparison: Direct benchmark rivals: MiMo-V2.5-Pro (Xiaomi) vs GLM 5.1 (Z.ai) with overlapping confidence intervals Inclusion does not recommend either model.

MiMo-V2.5-Pro

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1465.1

Balanced effective USD / 1M tokens · lower is better

$0.544/M

Price under confirmation since Oct 21, 2026
GLM 5.1

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1462.6

Balanced effective USD / 1M tokens · lower is better

$1.83/M

Price under confirmation since Oct 21, 2026

In-Depth Analysis

MiMo-V2.5-Pro vs GLM 5.1: Measured Technical Breakdown

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

Measured Capability (LMArena)
MiMo-V2.5-Pro leads by 2.5 Elo

MiMo-V2.5-Pro: 1465.1 ±3.54 · GLM 5.1: 1462.6 ±3.59 Intervals overlap — not a guaranteed quality advantage

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 & RatioMiMo-V2.5-ProGLM 5.1Cost DifferenceAdvantage
Balanced 3 tokens in per 1 out$0.544 /1M tokens$1.83 /1M tokens$1.28 (70% diff)MiMo-V2.5-Pro costs less
Summarise Huge input, tiny output$0.334 /1M tokens$1.15 /1M tokens$0.816 (71% diff)MiMo-V2.5-Pro costs less
Chat Long cached system prompt$0.480 /1M tokens$2.03 /1M tokens$1.55 (76% diff)MiMo-V2.5-Pro costs less
Code gen Output dominates$0.661 /1M tokens$2.67 /1M tokens$2.01 (75% diff)MiMo-V2.5-Pro costs less
Agentic Context replayed each turn$0.244 /1M tokens$1.22 /1M tokens$0.978 (80% diff)MiMo-V2.5-Pro costs less

Architectural & Operational Specifications

SpecificationMiMo-V2.5-ProGLM 5.1Operational Note
Context window1,050,000 tokens204,800 tokensMiMo-V2.5-Pro has a 5.1× larger context window
Max output generation945,000 tokens131,072 tokensMiMo-V2.5-Pro allows longer single-turn completions
Base input price$0.435 / 1M tokens$1.19 / 1M tokensMiMo-V2.5-Pro input rate is cheaper
Base output price$0.870 / 1M tokens$3.74 / 1M tokensMiMo-V2.5-Pro output rate is cheaper
Cached input price$0.0036 / 1M tokens$0.600 / 1M tokensMiMo-V2.5-Pro offers lower cached prompt cost
Reasoning & thinking tokensSupported; standard ratesSupported; standard rates—
Input modalitiestexttext—
Tool calling / function useSupportedSupported—
Open weights & licenceProprietary (API-only)Proprietary (API-only)—
Provider & reseller marketXiaomi · 6 provider offers (1.71× spread)Z.ai · 13 provider offers (1.45× spread)—

When to Choose MiMo-V2.5-Pro

  • Higher measured general capability (1465.1 Elo on human evaluation).
  • Larger context window (1,050,000 tokens) for long-document or repo-level context.
  • Higher maximum completion limit (945,000 tokens).
  • Direct integration with Xiaomi tooling and API features.

When to Choose GLM 5.1

  • Direct integration with Z.ai tooling and API features.
Risks when moving from MiMo-V2.5-Pro → GLM 5.1:
  • Context window: 1,050,000 → 204,800 tokens.
  • Output limit: 945,000 → 131,072 tokens.

Detailed Evidence Passports & Independent Audits

MiMo-V2.5-Pro Full model card, pricing ladder & provider offers → GLM 5.1 Full model card, pricing ladder & provider offers → Alternatives to MiMo-V2.5-Pro Better-and-cheaper dominance audit → Alternatives to GLM 5.1 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

MiMo-V2.5-Pro has an equal-or-higher measured score and an equal-or-lower estimated price. No known model-capability loss from GLM 5.1; endpoint and task checks still apply. The reported score intervals overlap; this is not an established quality improvement.

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.

  • MiMo-V2.5-Pro · Meets the stated model requirements
  • GLM 5.1 · Meets the stated model requirements
Material differences and unknown evidence. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA MiMo-V2.5-ProB GLM 5.1
LMArena general score (Elo)1,465.11,462.6Δ vs A: -2.5 Elo
Score interval half-width (± Elo)3.543.59Δ vs A: +0.05 Elo
LMArena document score (Elo)UnknownUnknown
Estimated effective USD / 1M tokens$0.54375$1.8275Δ vs A: +1.28375 USD/M
Base input USD / 1M tokens$0.435$1.19Δ vs A: +0.755 USD/M
Base output USD / 1M tokens$0.87$3.74Δ vs A: +2.87 USD/M
Base cached-input USD / 1M tokens$0.0036$0.6Δ vs A: +0.5964 USD/M
Context window (tokens)1,050,000204,800Δ vs A: -845,200 tokens
Output limit (tokens)945,000131,072Δ vs A: -813,928 tokens
Open weightsUnknownUnknown
Recorded licenceUnknownUnknown
Model providerXiaomiZ.ai
Distinct sellers in the catalogue613Δ vs A: +7
Recorded retirement dateUnknownUnknown
7 shared measured facts
Shared measured facts. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA MiMo-V2.5-ProB GLM 5.1
Benchmark reasoning effortdefaultdefault
Price statusUnder confirmation since 2026-10-21Under confirmation since 2026-10-21
Input modalitiestexttext
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

MiMo-V2.5-Pro

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$0.435$0.87$0.0036

GLM 5.1

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$1.19$3.74$0.6
Migration preflight · compare each candidate with model A

Keeping MiMo-V2.5-Pro is a valid decision. No pairwise comparison certifies a drop-in replacement.

MiMo-V2.5-Pro → GLM 5.1

Known loss or failed requirement

  • Context window: 1,050,000 → 204,800 tokens.
  • Output limit: 945,000 → 131,072 tokens.
  • Provider endpoint, serving precision, availability and task success need your own verification. Catalogue model capabilities do not certify an endpoint.
Check alternatives to MiMo-V2.5-Pro

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.

  • MiMo-V2.5-ProEstimated total: $1.305 1,000 full attempts · base rates
  • GLM 5.1Estimated total: $4.25 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 tokensMiMo-V2.5-ProGLM 5.1Coverage
2,000$1.305Lowest known$4.25All computed
4,096$2.21676Lowest known$6.74424All computed
32,768$14.68908Lowest known$40.86392All computed
131,072$57.45132Lowest known$157.84568All computed
Vary output; hold input, requests, cache and retries fixed.
Output tokensMiMo-V2.5-ProGLM 5.1
128$0.98136$2.85872
2,048$2.65176$10.03952
8,192$7.99704$33.01808

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 MiMo-V2.5-Pro a better-and-cheaper replacement for GLM 5.1?

MiMo-V2.5-Pro has an equal-or-higher measured score and an equal-or-lower estimated price. No known model-capability loss from GLM 5.1; endpoint and task checks still apply. The reported score intervals overlap; this is not an established quality improvement.

Which model is higher rated: MiMo-V2.5-Pro or GLM 5.1?

MiMo-V2.5-Pro scores higher on LMArena general (1465.1 ±3.5) compared to GLM 5.1 (1462.6 ±3.6), a margin of 2.5 Elo points. However, because the 95% confidence intervals overlap, this does not establish an unconditional superiority across all tasks.

Which model is more cost-effective: MiMo-V2.5-Pro or GLM 5.1?

On a standard balanced workload (3 prompt tokens per 1 generated token), MiMo-V2.5-Pro costs $0.544 (price under confirmation since 2026-10-21) per 1M tokens versus $1.83 (price under confirmation since 2026-10-21) for GLM 5.1. GLM 5.1 is approximately 3.4× the price of MiMo-V2.5-Pro. Base input rates are $0.435 for MiMo-V2.5-Pro and $1.19 for GLM 5.1; base output rates are $0.870 for MiMo-V2.5-Pro and $3.74 for GLM 5.1.

How do their context windows and generation limits compare?

MiMo-V2.5-Pro provides a 1,050,000-token context window with a maximum output limit of 945,000 tokens. GLM 5.1 provides a 204,800-token context window with a maximum output of 131,072 tokens. MiMo-V2.5-Pro offers a 5.1× larger context capacity, making it suitable for larger repositories and multi-document workflows.

What capabilities are lost when switching between MiMo-V2.5-Pro and GLM 5.1?

Switching from MiMo-V2.5-Pro to GLM 5.1 involves: Context window: 1,050,000 → 204,800 tokens; Output limit: 945,000 → 131,072 tokens. Run test suites on your specific prompt templates and tool declarations before cutting over production traffic.

Can MiMo-V2.5-Pro or GLM 5.1 be self-hosted or deployed privately?

Both MiMo-V2.5-Pro and GLM 5.1 are proprietary cloud models offered via managed API endpoints. MiMo-V2.5-Pro is served by Xiaomi and 6 catalogue offers, while GLM 5.1 is served by Z.ai and 13 catalogue offers.

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
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