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Model comparison

Kimi K2.5 vs MiMo-V2-Omni

Updated August 1, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

20 confirmed releases in the last 30 daysSee provider release alerts
Kimi K2.5

Moonshot AI

58.8/100

Supported · Public rank #61

90% interval 51.3–66.2

MiMo-V2-Omni

Xiaomi

62.2/100

Supported · Public rank #43

90% interval 52.3–72.0

MiMo-V2-Omni has the higher public score estimate, 62.16 versus 58.75, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Long documents

    Prompts that approach the documented context limit

    MiMo-V2-Omni

    MiMo-V2-Omni has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: rate-fallback

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
2
Kimi K2.5 only
43
MiMo-V2-Omni only
0
Like-for-like categories
0 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Coding

Directional only
Kimi K2.5
59.4
MiMo-V2-Omni
74.8
Weighted basis
4 vs 1 rows
Reading
Directional only

Agentic

Not comparable
Kimi K2.5
55.0
MiMo-V2-Omni
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Kimi K2.5
61.0
MiMo-V2-Omni
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Kimi K2.5
56.9
MiMo-V2-Omni
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Math

Not comparable
Kimi K2.5
60.6
MiMo-V2-Omni
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Kimi K2.5
82.3
MiMo-V2-Omni
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Kimi K2.5
78.5
MiMo-V2-Omni
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Kimi K2.5
93.9
MiMo-V2-Omni
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Kimi K2.5
$0.0021
Fits in one request
MiMo-V2-Omni
API rate not published
Fits in one request

MiMo-V2-Omni has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Kimi K2.5
$0.039
Fits in one request
MiMo-V2-Omni
API rate not published
Fits in one request

MiMo-V2-Omni has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Kimi K2.5
$0.162
Fits in one request
Cached input priced at the published list-input rate
MiMo-V2-Omni
API rate not published
Fits in one request
Cached-input rate unavailable

Kimi K2.5 has no published cached-input rate, so cached tokens use its listed input rate. MiMo-V2-Omni has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

Maximum documented context; output-token limits may be lower.

Kimi K2.5

256K

MiMo-V2-Omni

262K

API model ID

Kimi K2.5

Not sourced

MiMo-V2-Omni

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Kimi K2.5

Not published

MiMo-V2-Omni

No comparable hosted API rate

Documented inputs

Kimi K2.5

Not sourced

MiMo-V2-Omni

Not sourced

Documented outputs

Kimi K2.5

Not sourced

MiMo-V2-Omni

Not sourced

Provider availability

Kimi K2.5

Not sourced

MiMo-V2-Omni

Not sourced

Reasoning profile

Kimi K2.5

Non-Reasoning

MiMo-V2-Omni

Reasoning

Weight access

Kimi K2.5

Open Weight

MiMo-V2-Omni

Proprietary

License

Kimi K2.5

Open Weight

MiMo-V2-Omni

Proprietary

Release date

Kimi K2.5

2026-02-01

MiMo-V2-Omni

2026-03-18

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
MiMo-V2-Omni has the higher public score estimate, 62.16 versus 58.75, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
MiMo-V2-Omni has the larger documented window (262K).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Kimi K2.5
API / mo$2,700
Self-host / mo$5,221
Break-even132M/day
MiMo-V2-Omni
API / mo$0
Self-host / moNot listed
Break-even
Proprietary model — self-hosting not applicable.
Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence45 rows

Agentic

  • Terminal-Bench 2.0

    Kimi K2.550.8%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • BrowseComp

    Kimi K2.560.6%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • Kimi K2.552.3%
    MiMo-V2-Omni45.2%

    Kimi K2.5 leads this result

  • QwenClawBench

    Kimi K2.554.3%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • τ³-bench results

    Kimi K2.565.7%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • DeepSearchQA

    Kimi K2.577.1%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • DeepPlanning

    Kimi K2.514.4%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • Toolathlon

    Kimi K2.527.8%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • MCP Atlas

    Kimi K2.529.5%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • MCP-Tasks

    Kimi K2.559.1%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • WideResearch

    Kimi K2.572.7%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • Gert Labs

    Kimi K2.545.88%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • ResearchClawBench

    Kimi K2.514.0%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • JobBench

    Kimi K2.58.7%
    Source
    MiMo-V2-Omni

    Not directly comparable

Coding

  • SWE-bench Verified

    Kimi K2.576.8%
    Source
    MiMo-V2-Omni74.8%
    Source

    Kimi K2.5 leads this result

  • SWE-bench Verified*

    Kimi K2.570.8%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • LiveCodeBench v6

    Kimi K2.585.0%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • SWE-bench Pro

    Kimi K2.550.7%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • SWE Multilingual

    Kimi K2.573%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • SWE-Rebench

    Kimi K2.558.5%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • React Native Evals

    Kimi K2.577.2%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • SciCode

    Kimi K2.548.7%
    Source
    MiMo-V2-Omni

    Not directly comparable

Reasoning

  • LongBench v2

    Kimi K2.561%
    Source
    MiMo-V2-Omni

    Not directly comparable

Knowledge

  • GPQA

    Kimi K2.587.6%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • GPQA-D

    Kimi K2.587.6%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • SuperGPQA

    Kimi K2.569.2%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • MMLU-Pro

    Kimi K2.587.1%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • MMLU-Pro (Arcee)

    Kimi K2.587.1%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • HLE

    Kimi K2.530.1%
    Source
    MiMo-V2-Omni

    Not directly comparable

Math

  • AIME 2025

    Kimi K2.596.1%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • AIME26

    Kimi K2.595.8%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • AIME25 (Arcee)

    Kimi K2.596.3%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • HMMT Feb 2025

    Kimi K2.595.4%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • HMMT Nov 2025

    Kimi K2.591.1%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • HMMT Feb 2026

    Kimi K2.587.1%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • MMAnswerBench

    Kimi K2.581.8%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Kimi K2.527.900%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Kimi K2.54.200%
    Source
    MiMo-V2-Omni

    Not directly comparable

Multilingual

  • MMLU-ProX

    Kimi K2.582.3%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • NOVA-63

    Kimi K2.556.0%
    Source
    MiMo-V2-Omni

    Not directly comparable

Multimodal

  • MMMU-Pro

    Kimi K2.578.5%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • Video-MME

    Kimi K2.587.4%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • MMVU

    Kimi K2.580.4%
    Source
    MiMo-V2-Omni

    Not directly comparable

  • VideoMMMU

    Kimi K2.586.6%
    Source
    MiMo-V2-Omni

    Not directly comparable

Instruction following

  • IFEval

    Kimi K2.593.9%
    Source
    MiMo-V2-Omni

    Not directly comparable

Frequently asked questions

Which is better, Kimi K2.5 or MiMo-V2-Omni?

MiMo-V2-Omni has the higher public score estimate, 62.16 versus 58.75, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Kimi K2.5 or MiMo-V2-Omni?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Kimi K2.5 or MiMo-V2-Omni?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Kimi K2.5 or MiMo-V2-Omni?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Kimi K2.5 or MiMo-V2-Omni?

MiMo-V2-Omni has the larger documented context window: 262K, compared with 256K.

Related comparisons

Last updated August 1, 2026

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