Long documents
Prompts that approach the documented context limit
Kimi K3
Kimi K3 has the larger documented context window.
Confidence: documented
Agents-A1 vs Kimi K3
Updated October 2, 2026. Rank says Kimi K3 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Decision reading
Kimi K3 has the higher public point estimate, 72.14 versus 53.43. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- Agents-A1 only
- 4
- Kimi K3 only
- 46
- Like-for-like categories
- 0 / 8
Estimated: Agents-A1 · Supported: Kimi K3. Conditional ranges do not establish rank confidence.How the comparison works
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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Agents-A1 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Agents-A1 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedChat 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-ratesCache-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: listed-ratesRepository 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
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
—Agents-A161.4Kimi K3
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 Agents-A1 only
2 Shared
46 Kimi K3 only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
BrowseCompAgentic
Normalized gap 15.7Agents-A1:75.5%Kimi K3:91.2%HLEKnowledge
Normalized gap 8.4Agents-A1:47.6%Kimi K3:56%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Agentic
Directional only- Agents-A1
- 44.3
- Estimated · #48/119
- Kimi K3
- 68.1
- Supported · #8/119
- Basis
- BenchAlign v5.8 lane · 3 vs 12 public rows
- Reading
- Directional only
Coding
Not comparable- Agents-A1
- Not ranked
- Kimi K3
- 61.4
- Supported · #18/144
- Basis
- BenchAlign v5.8 lane · 0 vs 14 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Agents-A1
- 35.2
- Unranked · 1 rankable row
- Kimi K3
- 65.8
- #18/27
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Agents-A1
- Not ranked
- Kimi K3
- 89.4
- #1/49
- Basis
- Provisional lane · 0 vs 3 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Agents-A1
- Not ranked
- Kimi K3
- 67.9
- Supported · #18/171
- Basis
- BenchAlign v5.8 lane · 1 vs 6 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Agents-A1
- Not ranked
- Kimi K3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Agents-A1
- Not ranked
- Kimi K3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Agents-A1
- Not ranked
- Kimi K3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Agents-A1 | Kimi K3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 44.3Estimated · #48/119 | 68.1Supported · #8/119 | Directional onlyBenchAlign v5.8 lane · 3 vs 12 public rows | Directional only |
| Coding | Not ranked | 61.4Supported · #18/144 | Not comparableBenchAlign v5.8 lane · 0 vs 14 public rows | Not comparable |
| Reasoning | 35.2Unranked · 1 rankable row | 65.8#18/27 | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 89.4#1/49 | Not comparableProvisional lane · 0 vs 3 weighted rows | Not comparable |
| Knowledge | Not ranked | 67.9Supported · #18/171 | Not comparableBenchAlign v5.8 lane · 1 vs 6 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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
- Agents-A1
- API rate not published
- Fits in one request
- Kimi K3
- $0.0105
- Fits in one request
Agents-A1 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Agents-A1
- API rate not published
- Fits in one request
- Kimi K3
- $0.195
- Fits in one request
Agents-A1 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Agents-A1
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Kimi K3
- $0.27
- Fits in one request
Agents-A1 has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationAgents-A1Kimi K3
Context window
Maximum documented context; output-token limits may be lower.
Agents-A1
262K
Kimi K3
1.05M
API model ID
Agents-A1
Not sourced
Kimi K3
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Agents-A1
No comparable hosted API rate
Kimi K3
$0.3 per 1M cached input tokens
Documented inputs
Agents-A1
Not sourced
Kimi K3
Not sourced
Documented outputs
Agents-A1
Not sourced
Kimi K3
Not sourced
Provider availability
Agents-A1
Not sourced
Kimi K3
Not sourced
Reasoning profile
Agents-A1
Reasoning
Kimi K3
Reasoning
Weight access
Agents-A1
Open Weight
Kimi K3
Pending
License
Agents-A1
Open Weight
Kimi K3
Pending
Release date
Agents-A1
2026-06-26
Kimi K3
2026-07-16
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
- Kimi K3 has the higher public point estimate, 72.14 versus 53.43. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Kimi K3 has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Agents-A1 or Kimi K3?
Kimi K3 has the higher public point estimate, 72.14 versus 53.43. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Agents-A1 or Kimi K3?
Agents-A1 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Agents-A1 or Kimi K3?
Kimi K3 scores higher for agentic tasks on the public lane, 68.1 to 44.3. Agents-A1 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Agents-A1 or Kimi K3?
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, Agents-A1 or Kimi K3?
Kimi K3 has the larger documented context window: 1.05M, compared with 262K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence52 rows
Agentic
BrowseComp
Agents-A175.5%SourceKimi K391.2%SourceKimi K3 leads this result
HLE w/ tools
Agents-A147.6%SourceKimi K3—Not directly comparable
VITA-Bench
Agents-A138.8%SourceKimi K3—Not directly comparable
Terminal-Bench 2.1
Agents-A1—Kimi K388.3%SourceNot directly comparable
DeepSearchQA
Agents-A1—Kimi K395.0%SourceNot directly comparable
Toolathlon-Verified
Agents-A1—Kimi K373.2%SourceNot directly comparable
MCP Atlas
Agents-A1—Kimi K384.2%SourceNot directly comparable
AutomationBench
Agents-A1—Kimi K330.8%SourceNot directly comparable
JobBench
Agents-A1—Kimi K352.9%SourceNot directly comparable
APEX-Agents
Agents-A1—Kimi K337.6%SourceNot directly comparable
SpreadsheetBench 2
Agents-A1—Kimi K334.8%SourceNot directly comparable
DECK-Bench
Agents-A1—Kimi K373.5%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Agents-A1—Kimi K380.9%SourceNot directly comparable
ApprenticeBench
Agents-A1—Kimi K318%SourceNot directly comparable
Coding
DeepSWE
Agents-A1—Kimi K367.5%SourceNot directly comparable
CursorBench 3.2
Agents-A1—Kimi K360.8%SourceNot directly comparable
FrontierSWE
Agents-A1—Kimi K381.2%SourceNot directly comparable
ProgramBench
Agents-A1—Kimi K377.8%SourceNot directly comparable
Kimi Code Bench v2
Agents-A1—Kimi K372.9%SourceNot directly comparable
sweMarathon
Agents-A1—Kimi K342%SourceNot directly comparable
PostTrain Bench
Agents-A1—Kimi K336.6%SourceNot directly comparable
MLS-Bench Lite
Agents-A1—Kimi K348.3%SourceNot directly comparable
VulcanBench v3
Agents-A1—Kimi K373.7%SourceNot directly comparable
OpenHarmony Bench
Agents-A1—Kimi K357.3%SourceNot directly comparable
FrontierSWE v2
Agents-A1—Kimi K325.9%SourceNot directly comparable
LiveCodeBench (Vals)
Agents-A1—Kimi K387.2%SourceNot directly comparable
SWE-bench (Vals)
Agents-A1—Kimi K393.4%SourceNot directly comparable
PostTrainBench v1.1
Agents-A1—Kimi K332.0%SourceNot directly comparable
Reasoning
Multimodal
OfficeQA Pro
Agents-A1—Kimi K363.3%SourceNot directly comparable
MMMU-Pro
Agents-A1—Kimi K381.6%SourceNot directly comparable
MMMU-Pro w/ Python
Agents-A1—Kimi K383.4%SourceNot directly comparable
CharXiv w/o tools
Agents-A1—Kimi K384.8%SourceNot directly comparable
CharXiv
Agents-A1—Kimi K391.3%SourceNot directly comparable
MathVision
Agents-A1—Kimi K394.3%SourceNot directly comparable
MathVision w/ Python
Agents-A1—Kimi K397.8%SourceNot directly comparable
BabyVision w/ Python
Agents-A1—Kimi K385.7%SourceNot directly comparable
ZeroBench
Agents-A1—Kimi K323.0%SourceNot directly comparable
ZeroBench w/ Python
Agents-A1—Kimi K341.0%SourceNot directly comparable
WorldVQA ForceAnswer
Agents-A1—Kimi K351.0%SourceNot directly comparable
OmniDocBench
Agents-A1—Kimi K391.1%SourceNot directly comparable
PerceptionBench
Agents-A1—Kimi K358.5%SourceNot directly comparable
Knowledge
HLE
Agents-A147.6%SourceKimi K356%SourceKimi K3 leads this result
GPQA
Agents-A1—Kimi K393.5%SourceNot directly comparable
GPQA-D
Agents-A1—Kimi K393.5%SourceNot directly comparable
HLE w/o tools
Agents-A1—Kimi K343.5%SourceNot directly comparable
GPQA Diamond (Vals)
Agents-A1—Kimi K392.9%SourceNot directly comparable
MMLU-Pro (Vals)
Agents-A1—Kimi K388.0%SourceNot directly comparable
Instruction following
52 public results · 2 shared
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Last updated October 2, 2026