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BenchLM
Moonshot AI logo
Model A
Kimi K2.5 (Reasoning)

Moonshot AI

60.0/100

Estimated · Public rank #67

90% interval 48.5–71.5

Kimi K2.5 (Reasoning) vs Qwen3.8-Flash-Next

Updated August 26, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Alibaba logo
Model B
Qwen3.8-Flash-Next

Alibaba

67.5/100

Estimated · Public rank #25

90% interval 57.7–77.4

Decision reading

Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 60.03, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 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

    Qwen3.8-Flash-Next

    Qwen3.8-Flash-Next 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

    No shared weighted benchmark basis supports a winner.

    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
1
Kimi K2.5 (Reasoning) only
8
Qwen3.8-Flash-Next only
23
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.

Knowledge

Directional only
Kimi K2.5 (Reasoning)
87.2
Qwen3.8-Flash-Next
43.4
Weighted basis
2 vs 2 rows
Reading
Directional only

Agentic

Not comparable
Kimi K2.5 (Reasoning)
55.0
Qwen3.8-Flash-Next
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Kimi K2.5 (Reasoning)
76.8
Qwen3.8-Flash-Next
62.5
Weighted basis
1 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Kimi K2.5 (Reasoning)
Not measured
Qwen3.8-Flash-Next
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Kimi K2.5 (Reasoning)
Not measured
Qwen3.8-Flash-Next
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Kimi K2.5 (Reasoning)
Not measured
Qwen3.8-Flash-Next
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Kimi K2.5 (Reasoning)
78.5
Qwen3.8-Flash-Next
90.6
Weighted basis
1 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Kimi K2.5 (Reasoning)
Not measured
Qwen3.8-Flash-Next
81.3
Weighted basis
0 vs 1 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 (Reasoning)
$0.0021
Fits in one request
Qwen3.8-Flash-Next
Self-hosted; infrastructure cost varies
Fits in one request

Qwen3.8-Flash-Next has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Kimi K2.5 (Reasoning)
$0.039
Fits in one request
Qwen3.8-Flash-Next
Self-hosted; infrastructure cost varies
Fits in one request

Qwen3.8-Flash-Next has no comparable published API token rate.

Cache-heavy agent loop

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

Kimi K2.5 (Reasoning)
$0.162
Fits in one request
Cached input priced at the published list-input rate
Qwen3.8-Flash-Next
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.8-Flash-Next 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 (Reasoning)

256K

Qwen3.8-Flash-Next

262K

Qwen3.8-Flash-Next model card

Cached-input rate

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

Kimi K2.5 (Reasoning)

Not published

Qwen3.8-Flash-Next

No comparable hosted API rate

Qwen3.8-Flash-Next model card

Documented inputs

Kimi K2.5 (Reasoning)

Not sourced

Qwen3.8-Flash-Next

Not sourced

Documented outputs

Kimi K2.5 (Reasoning)

Not sourced

Qwen3.8-Flash-Next

Not sourced

Provider availability

Kimi K2.5 (Reasoning)

Not sourced

Qwen3.8-Flash-Next

Not sourced

Reasoning profile

Kimi K2.5 (Reasoning)

Reasoning

Qwen3.8-Flash-Next

Reasoning

Weight access

Kimi K2.5 (Reasoning)

Proprietary

Qwen3.8-Flash-Next

Open Weight

License

Kimi K2.5 (Reasoning)

Proprietary

Qwen3.8-Flash-Next

Open Weight

Release date

Kimi K2.5 (Reasoning)

2026-02-01

Qwen3.8-Flash-Next

2026-08-26

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
Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 60.03, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Qwen3.8-Flash-Next has the larger documented window (262K).

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

Benchmark evidence

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

Browse raw public benchmark evidence32 rows

Agentic

  • Terminal-Bench 2.0

    Kimi K2.5 (Reasoning)50.8%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • BrowseComp

    Kimi K2.5 (Reasoning)60.6%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • Gert Labs

    Kimi K2.5 (Reasoning)32.58%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • CoWorkBench

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next73.9%
    Source

    Not directly comparable

  • JobBench

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next55.7%
    Source

    Not directly comparable

  • Agents' Last Exam

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next51.2%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next73.5%
    Source

    Not directly comparable

  • AndroidWorld

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next84.5%
    Source

    Not directly comparable

  • OSWorld 2.0

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next19.4%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Kimi K2.5 (Reasoning)76.8%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • Vibe Code Bench

    Kimi K2.5 (Reasoning)17.54%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • SWE-bench Pro

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next62.5%
    Source

    Not directly comparable

  • SWE Multilingual

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next81%
    Source

    Not directly comparable

  • NL2Repo

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next48.1%
    Source

    Not directly comparable

  • deepSwe

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next58.7%
    Source

    Not directly comparable

  • LiveCodeBench v6

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next91.9%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Kimi K2.5 (Reasoning)87.6%
    Source
    Qwen3.8-Flash-Next91.7%
    Source

    Qwen3.8-Flash-Next leads this result

  • MMLU-Pro

    Kimi K2.5 (Reasoning)87.1%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • GPQA-D

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next91.7%
    Source

    Not directly comparable

  • HLE

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next35.9%
    Source

    Not directly comparable

  • HLE w/o tools

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next35.9%
    Source

    Not directly comparable

Math

  • AIME 2025

    Kimi K2.5 (Reasoning)96.1%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

Multimodal

  • MMMU-Pro

    Kimi K2.5 (Reasoning)78.5%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • Vision2Web

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next64.0%
    Source

    Not directly comparable

  • ERQA

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next72.3%
    Source

    Not directly comparable

  • LVBench

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next76.6%
    Source

    Not directly comparable

  • RealWorldQA

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next88.5%
    Source

    Not directly comparable

  • MathVision

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next90.6%
    Source

    Not directly comparable

  • MathVision w/ Python

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next95.7%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next84.6%
    Source

    Not directly comparable

  • CharXiv

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next90.6%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Kimi K2.5 (Reasoning)—
    Qwen3.8-Flash-Next81.3%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Kimi K2.5 (Reasoning) or Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 60.03, 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 (Reasoning) or Qwen3.8-Flash-Next?

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

Which is better for agentic tasks, Kimi K2.5 (Reasoning) or Qwen3.8-Flash-Next?

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 (Reasoning) or Qwen3.8-Flash-Next?

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 (Reasoning) or Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next has the larger documented context window: 262K, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 26, 2026

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