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Model comparison

Agents-A1 vs Kimi K2.5

Data verified

Head-to-head evidence from 4 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

Agents-A1
InternScience
N/A
No comparison
Kimi K2.5
Moonshot AI
59.66/100
2 category wins2 category wins

Public leaderboard positions: Agents-A1 unranked (Not scored); Kimi K2.5 #54 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Agents-A1 and Kimi K2.5 share 4 comparable benchmark results. 4 of 8 categories are comparable. 2 results are unique to Agents-A1; 59 to Kimi K2.5.

Updated July 23, 2026
Shared results
4
Agents-A1 only
2
Kimi K2.5 only
59
Comparable categories
4 / 8

Treat this as a split decision. Agents-A1 makes more sense if agentic is the priority or you need the larger 262K context window; Kimi K2.5 is the better fit if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.

Confidence note. This is a partial-evidence comparison with 4 shared benchmark results across 4 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

Agents-A1 and Kimi K2.5 finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.

Agents-A1 is the reasoning model in the pair, while Kimi K2.5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Agents-A1 gives you the larger context window at 262K, compared with 256K for Kimi K2.5.

Category breakdown

Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.

Category scores and score margins for Agents-A1 and Kimi K2.5
CategoryAgents-A1ΔKimi K2.5
AgenticAgents-A175.5Margin← 20.5Kimi K2.555.0
KnowledgeAgents-A147.6Margin→ 9.3Kimi K2.556.9
Inst. FollowingAgents-A194.8Margin← 0.9Kimi K2.593.9
ReasoningAgents-A160.2Margin→ 0.8Kimi K2.561.0
CodingAgents-A1Not measuredMarginNo overlapKimi K2.559.4
MathAgents-A1Not measuredMarginNo overlapKimi K2.560.6
MultilingualAgents-A1Not measuredMarginNo overlapKimi K2.582.3
MultimodalAgents-A1Not measuredMarginNo overlapKimi K2.578.5

Decisive benchmark drivers

The largest measured benchmark gaps in this matchup, with exact reported values.

More
A · Agents-A1B · Kimi K2.5

  1. HLE

    Knowledge
    Source ↗
    A 47.6%B 30.1%
    Winner: Agents-A1Δ 17.5
    HLE: Agents-A1 scored 47.6%; Kimi K2.5 scored 30.1%. Agents-A1 wins this benchmark.

  2. BrowseComp

    Agentic
    Source ↗
    A 75.5%B 60.6%
    Winner: Agents-A1Δ 14.9
    BrowseComp: Agents-A1 scored 75.5%; Kimi K2.5 scored 60.6%. Agents-A1 wins this benchmark.

  3. IFEval

    Inst. Following
    Source ↗
    A 94.8%B 93.9%
    Winner: Agents-A1Δ 0.9
    IFEval: Agents-A1 scored 94.8%; Kimi K2.5 scored 93.9%. Agents-A1 wins this benchmark.

  4. LongBench v2

    Reasoning
    Source ↗
    A 60.2%B 61%
    Winner: Kimi K2.5Δ 0.8
    LongBench v2: Agents-A1 scored 60.2%; Kimi K2.5 scored 61%. Kimi K2.5 wins this benchmark.

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricAgents-A1Kimi K2.5Comparison
Input / output priceUSD per 1M tokensAgents-A1Not availableKimi K2.5$0.6 input / $3 outputA complete price comparison is not available.
Generation speedtokens per secondAgents-A1Not availableKimi K2.545 tok/sA complete speed comparison is not available.
First-answer latencyseconds to first tokenAgents-A1Not availableKimi K2.52.38 sA complete latency comparison is not available.
Context windowmaximum listed tokensAgents-A1262KKimi K2.5256KAgents-A1 lists the larger context window.

Benchmark Deep Dive

AgenticAgents-A1 wins
BenchmarkAgents-A1Kimi K2.5Result
BrowseCompSource 75.5%60.6%Agents-A1 leads
HLE w/ toolsSource 47.6%—Not comparable
VITA-BenchSource 38.8%—Not comparable
Terminal-Bench 2.0Source —50.8%Not comparable
Claw-EvalSource —52.3%Not comparable
QwenClawBenchSource —54.3%Not comparable
τ³-bench resultsSource —65.7%Not comparable
DeepSearchQASource —77.1%Not comparable
DeepPlanningSource —14.4%Not comparable
ToolathlonSource —27.8%Not comparable
MCP AtlasSource —29.5%Not comparable
MCP-TasksSource —59.1%Not comparable
WideResearchSource —72.7%Not comparable
τ²-bench resultsSource —95.9%Not comparable
APEX-Agents-AASource —11.5%Not comparable
Gert LabsSource —45.88%Not comparable
ResearchClawBenchSource —14.0%Not comparable
JobBenchSource —8.7%Not comparable
AA Agentic IndexSource —21.7%Not comparable
GDPval-AASource —25.4%Not comparable
GDPval-AASource —1009Not comparable
Coding
BenchmarkAgents-A1Kimi K2.5Result
SWE-bench VerifiedSource —76.8%Not comparable
SWE-bench Verified*Source —70.8%Not comparable
LiveCodeBench v6Source —85.0%Not comparable
SWE-bench ProSource —50.7%Not comparable
SWE MultilingualSource —73%Not comparable
SWE-RebenchSource —58.5%Not comparable
React Native EvalsSource —77.2%Not comparable
SciCodeSource —48.7%Not comparable
AA-SciCodeSource —49.0%Not comparable
AA Coding IndexSource —46.8%Not comparable
ReasoningKimi K2.5 wins
BenchmarkAgents-A1Kimi K2.5Result
LongBench v2Source 60.2%61%Kimi K2.5 leads
AA-LCRSource —65.3%Not comparable
CritPtSource —3.1%Not comparable
KnowledgeKimi K2.5 wins
BenchmarkAgents-A1Kimi K2.5Result
HLESource 47.6%30.1%Agents-A1 leads
GPQASource —87.6%Not comparable
GPQA-DSource —87.6%Not comparable
SuperGPQASource —69.2%Not comparable
MMLU-ProSource —87.1%Not comparable
MMLU-Pro (Arcee)Source —87.1%Not comparable
Artificial Analysis Intelligence IndexSource —35.4%Not comparable
AA-GPQA DiamondSource —87.9%Not comparable
AA-HLESource —29.4%Not comparable
AA-Omniscience IndexSource —-8.1%Not comparable
AA-Omniscience AccuracySource —34.3%Not comparable
AA-Omniscience Hallucination RateSource —64.6%Not comparable
Math
BenchmarkAgents-A1Kimi K2.5Result
AIME 2025Source —96.1%Not comparable
AIME26Source —95.8%Not comparable
AIME25 (Arcee)Source —96.3%Not comparable
HMMT Feb 2025Source —95.4%Not comparable
HMMT Nov 2025Source —91.1%Not comparable
HMMT Feb 2026Source —87.1%Not comparable
MMAnswerBenchSource —81.8%Not comparable
FrontierMath v2 (Tiers 1-3)Source —27.900%Not comparable
FrontierMath v2 (Tier 4)Source —4.200%Not comparable
Multilingual
BenchmarkAgents-A1Kimi K2.5Result
MMLU-ProXSource —82.3%Not comparable
NOVA-63Source —56.0%Not comparable
Multimodal
BenchmarkAgents-A1Kimi K2.5Result
MMMU-ProSource —78.5%Not comparable
Video-MMESource —87.4%Not comparable
MMVUSource —80.4%Not comparable
VideoMMMUSource —86.6%Not comparable
AA-MMMU-ProSource —75.4%Not comparable
Design Arena WebsiteSource —1279Not comparable
Inst. FollowingAgents-A1 wins
BenchmarkAgents-A1Kimi K2.5Result
IFEvalSource 94.8%93.9%Agents-A1 leads
AA-IFBenchSource —70.2%Not comparable
Frequently Asked Questions (5)

Which is better, Agents-A1 or Kimi K2.5?

Agents-A1 and Kimi K2.5 are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.

Which is better for knowledge tasks, Agents-A1 or Kimi K2.5?

Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 56.9 versus 47.6. Inside this category, HLE is the benchmark that creates the most daylight between them.

Which is better for reasoning, Agents-A1 or Kimi K2.5?

Kimi K2.5 has the edge for reasoning in this comparison, averaging 61 versus 60.2. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, Agents-A1 or Kimi K2.5?

Agents-A1 has the edge for agentic tasks in this comparison, averaging 75.5 versus 55. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.

Which is better for instruction following, Agents-A1 or Kimi K2.5?

Agents-A1 has the edge for instruction following in this comparison, averaging 94.8 versus 93.9. Inside this category, IFEval is the benchmark that creates the most daylight between them.

Self-host vs API cost

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

Agents-A1
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Kimi K2.5
API / mo$2,700
Self-host / mo$5,221
Break-even132M/day
Model the full break-even

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