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Model comparison · accepted catalogue facts 2026-10-21

Kimi K3 vs MiMo-V2.5-Pro

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 head-to-head: Kimi K3 (Moonshot AI) vs MiMo-V2.5-Pro (Xiaomi) on intelligence, speed, and pricing Inclusion does not recommend either model.

Kimi K3

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1475.6

Balanced effective USD / 1M tokens · lower is better

$3.05/M

Price under confirmation since Oct 13, 2026
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

In-Depth Analysis

Kimi K3 vs MiMo-V2.5-Pro: Measured Technical Breakdown

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

Measured Capability (LMArena)
Kimi K3 leads by 10.5 Elo

Kimi K3: 1475.6 ±4.6 · MiMo-V2.5-Pro: 1465.1 ±3.54 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 & RatioKimi K3MiMo-V2.5-ProCost DifferenceAdvantage
Balanced 3 tokens in per 1 out$3.05 /1M tokens$0.544 /1M tokens$2.50 (82% diff)MiMo-V2.5-Pro costs less
Summarise Huge input, tiny output$1.14 /1M tokens$0.334 /1M tokens$0.808 (71% diff)MiMo-V2.5-Pro costs less
Chat Long cached system prompt$4.38 /1M tokens$0.480 /1M tokens$3.90 (89% diff)MiMo-V2.5-Pro costs less
Code gen Output dominates$6.28 /1M tokens$0.661 /1M tokens$5.62 (89% diff)MiMo-V2.5-Pro costs less
Agentic Context replayed each turn$2.01 /1M tokens$0.244 /1M tokens$1.77 (88% diff)MiMo-V2.5-Pro costs less

Architectural & Operational Specifications

SpecificationKimi K3MiMo-V2.5-ProOperational Note
Context window1,048,576 tokens1,050,000 tokens—
Max output generation943,718 tokens945,000 tokensMiMo-V2.5-Pro allows longer single-turn completions
Base input price$0.730 / 1M tokens$0.435 / 1M tokensMiMo-V2.5-Pro input rate is cheaper
Base output price$10.00 / 1M tokens$0.870 / 1M tokensMiMo-V2.5-Pro output rate is cheaper
Cached input price$0.550 / 1M tokens$0.0036 / 1M tokensMiMo-V2.5-Pro offers lower cached prompt cost
Reasoning & thinking tokensSupported; standard ratesSupported; standard rates—
Input modalitiestext, image, videotext—
Tool calling / function useSupportedSupported—
Open weights & licenceOpen weights (kimi-k3)Proprietary (API-only)Kimi K3 allows independent hosting or local deployment
Provider & reseller marketMoonshot AI · 25 provider offers (4.73× spread)Xiaomi · 6 provider offers (1.71× spread)—

When to Choose Kimi K3

  • Higher measured general capability (1475.6 Elo on human evaluation).
  • Open weights availability allows private self-hosting and on-premise execution.
  • Broader multimodal input support: text, image, video.
  • Direct integration with Moonshot AI tooling and API features.
Risks when moving from MiMo-V2.5-Pro → Kimi K3:
  • Context window: 1,050,000 → 1,048,576 tokens.
  • Output limit: 945,000 → 943,718 tokens.

When to Choose MiMo-V2.5-Pro

  • 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.
Risks when moving from Kimi K3 → MiMo-V2.5-Pro:
  • image input is not supported by the candidate.
  • video input is not supported by the candidate.

Detailed Evidence Passports & Independent Audits

Kimi K3 Full model card, pricing ladder & provider offers → MiMo-V2.5-Pro Full model card, pricing ladder & provider offers → Alternatives to Kimi K3 Better-and-cheaper dominance audit → Alternatives to MiMo-V2.5-Pro 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

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.

  • Kimi K3 · Meets the stated model requirements
  • MiMo-V2.5-Pro · Meets the stated model requirements
Material differences and unknown evidence. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA Kimi K3B MiMo-V2.5-Pro
LMArena general score (Elo)1,475.61,465.1Δ vs A: -10.5 Elo
Score interval half-width (± Elo)4.63.54Δ vs A: -1.06 Elo
Benchmark reasoning effortmaxdefault
LMArena document score (Elo)UnknownUnknown
Estimated effective USD / 1M tokens$3.0475$0.54375Δ vs A: -2.50375 USD/M
Base input USD / 1M tokens$0.73$0.435Δ vs A: -0.295 USD/M
Base output USD / 1M tokens$10$0.87Δ vs A: -9.13 USD/M
Base cached-input USD / 1M tokens$0.55$0.0036Δ vs A: -0.5464 USD/M
Price statusUnder confirmation since 2026-10-13Under confirmation since 2026-10-21
Context window (tokens)1,048,5761,050,000Δ vs A: +1,424 tokens
Output limit (tokens)943,718945,000Δ vs A: +1,282 tokens
Input modalitiestext, image, videotext
Open weightsSupportedUnknown
Recorded licencekimi-k3Unknown
Model providerMoonshot AIXiaomi
Distinct sellers in the catalogue206Δ vs A: -14
Recorded retirement dateUnknownUnknown
4 shared measured facts
Shared measured facts. Deltas compare each model with model A. Unknown facts remain visible.
Measured factA Kimi K3B MiMo-V2.5-Pro
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

Kimi K3

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$0.73$10$0.55

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
Migration preflight · compare each candidate with model A

Keeping Kimi K3 is a valid decision. No pairwise comparison certifies a drop-in replacement.

Kimi K3 → MiMo-V2.5-Pro

Known loss or failed requirement

  • 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 Kimi K3

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.

  • Kimi K3Estimated total: $6.46 1,000 full attempts · base rates
  • MiMo-V2.5-ProEstimated total: $1.305 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 tokensKimi K3MiMo-V2.5-ProCoverage
2,000$6.46$1.305Lowest knownAll computed
4,096$7.99008$2.21676Lowest knownAll computed
32,768$28.92064$14.68908Lowest knownAll computed
131,072$100.68256$57.45132Lowest knownAll computed
Vary output; hold input, requests, cache and retries fixed.
Output tokensKimi K3MiMo-V2.5-Pro
128$2.74$0.98136
2,048$21.94$2.65176
8,192$83.38$7.99704

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 Kimi K3 a better-and-cheaper replacement for MiMo-V2.5-Pro?

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.

Which model is higher rated: Kimi K3 or MiMo-V2.5-Pro?

Kimi K3 scores higher on LMArena general (1475.6 ±4.6) compared to MiMo-V2.5-Pro (1465.1 ±3.5), a margin of 10.5 Elo points. The measured confidence intervals do not overlap, demonstrating a statistically significant preference lead on human evaluations.

Which model is more cost-effective: Kimi K3 or MiMo-V2.5-Pro?

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 $3.05 (price under confirmation since 2026-10-13) for Kimi K3. Kimi K3 is approximately 5.6× the price of MiMo-V2.5-Pro. Base input rates are $0.730 for Kimi K3 and $0.435 for MiMo-V2.5-Pro; base output rates are $10.00 for Kimi K3 and $0.870 for MiMo-V2.5-Pro.

How do their context windows and generation limits compare?

Kimi K3 provides a 1,048,576-token context window with a maximum output limit of 943,718 tokens. MiMo-V2.5-Pro provides a 1,050,000-token context window with a maximum output of 945,000 tokens.

What capabilities are lost when switching between Kimi K3 and MiMo-V2.5-Pro?

Switching from Kimi K3 to MiMo-V2.5-Pro involves: image input is not supported by the candidate; video input is not supported by the candidate. Conversely, moving from MiMo-V2.5-Pro to Kimi K3 involves: Context window: 1,050,000 → 1,048,576 tokens; Output limit: 945,000 → 943,718 tokens. Run test suites on your specific prompt templates and tool declarations before cutting over production traffic.

Can Kimi K3 or MiMo-V2.5-Pro be self-hosted or deployed privately?

Kimi K3 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 (25 for Kimi K3, 6 for MiMo-V2.5-Pro).

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