Updated November 7, 2026. Rank says Kimi K3.1 Pro is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Kimi K3.1 Pro has the higher public point estimate, 75.03 versus 59.06. Their conditional score ranges overlap. These ranges do not establish rank confidence. 11 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Thinking Machines Lab
59.06/100
Estimated · Public rank #56
Conditional range 44.7–73.4
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.
Tool use, computer use, and multi-step task completion
Kimi K3.1 Pro
Kimi K3.1 Pro has the higher public agentic point estimate, 72.8 to 59.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Kimi K3.1 Pro
Kimi K3.1 Pro has the larger documented context window.
1K fresh input + 500 output tokens
Inkling 2 Small
Inkling 2 Small has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Inkling 2 Small
Inkling 2 Small has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Inkling 2 Small
Inkling 2 Small has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Kimi K3.1 Pro is not ranked on the public lane for coding, so no winner is named for coding.
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.
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.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
CharXivMultimodal
Normalized gap 5.7BrowseCompAgentic
Normalized gap 1.9GPQAKnowledge
Normalized gap 1.2Each 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.
| Category | Inkling 2 Small | Kimi K3.1 Pro | Basis | Reading |
|---|---|---|---|---|
| Agentic | 59.9Supported · #26/131 | 72.8Supported · #9/131 | Like-for-likeBenchAlign v5.8 lane · 5 vs 18 public rows | Kimi K3.1 Pro leads · intervals overlap |
| Multimodal | 86.6#12/59 | 100.0#1/59 | Directional onlyProvisional lane · 1 vs 3 weighted rows | Directional only |
| Knowledge | 60.4Estimated · #46/178 | 75.5Supported · #12/178 | Directional onlyBenchAlign v5.8 lane · 2 vs 5 public rows | Directional only |
| Coding | 59.7Supported · #20/157 | Not ranked | Not comparableBenchAlign v5.8 lane · 6 vs 8 public rows | Not comparable |
| Reasoning | 86.1Unranked · 2 rankable rows | 80.8Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted 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 | 85.9Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 1 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.
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.
1K fresh input + 500 output tokens
Inkling 2 Small has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Inkling 2 Small has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Inkling 2 Small has the lower modeled cost
Costs use the listed standard API rates.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Inkling 2 Small
Kimi K3.1 Pro
1.05M
Inkling 2 Small
Not sourced
Kimi K3.1 Pro
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Inkling 2 Small
$0.03 per 1M cached input tokens
Tinker models and pricingKimi K3.1 Pro
$0.2 per 1M cached input tokens
Inkling 2 Small
Not sourced
Kimi K3.1 Pro
Not sourced
Inkling 2 Small
Not sourced
Kimi K3.1 Pro
Not sourced
Inkling 2 Small
Not sourced
Kimi K3.1 Pro
Not sourced
Inkling 2 Small
Hybrid
Kimi K3.1 Pro
Reasoning
Inkling 2 Small
Proprietary
Kimi K3.1 Pro
Open Weight
Inkling 2 Small
Proprietary
Kimi K3.1 Pro
Open Weight
Inkling 2 Small
2026-11-05
Kimi K3.1 Pro
2026-10-23
Kimi K3.1 Pro has the higher public point estimate, 75.03 versus 59.06. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Kimi K3.1 Pro is not ranked on the public lane for coding, so no winner is named for coding.
Kimi K3.1 Pro has the higher public agentic tasks point estimate, 72.8 to 59.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.0008 on Inkling 2 Small and $0.007 on Kimi K3.1 Pro; repository review costs $0.018 and $0.13; the cache-heavy agent loop costs $0.022 and $0.18. Costs use the listed standard API rates.
Kimi K3.1 Pro has the larger documented context window: 1.05M, compared with 1M.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.1
Not directly comparable
BrowseComp
Kimi K3.1 Pro leads this result
DeepSearchQA
Not directly comparable
MCP Atlas
Not directly comparable
JobBench
Not directly comparable
Terminal-Bench 4.0
Not directly comparable
Terminal-Bench-Science 0.1
Not directly comparable
HLE w/ tools
Not directly comparable
BioMysteryBench
Not directly comparable
Agents' Last Exam
Not directly comparable
Agents' Last Exam (CLI)
Not directly comparable
APEX-Agents
Not directly comparable
Frontier Finance
Not directly comparable
HANDBOOK.md
Not directly comparable
OSWorld 2.0
Not directly comparable
Business Operation
Not directly comparable
Financial Modeling
Not directly comparable
Financial Research
Not directly comparable
Kimi Hallu Bench Hard (tools)
Not directly comparable
ExploitGym
Not directly comparable
MazeBench
Not directly comparable
OSWorld 2.1
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE Multilingual
Not directly comparable
sweMarathon
Kimi K3.1 Pro leads this result
Terminal-Bench 2.1
Not directly comparable
DeepSWE
Kimi K3.1 Pro leads this result
PostTrainBench v1.2
Kimi K3.1 Pro leads this result
ProgramBench (binary)
Not directly comparable
Kimi Code Bench v2
Not directly comparable
Kimi Code Bench 3.0 Long-horizon
Not directly comparable
Bug Hunt Bench
Not directly comparable
FrontierCode 2.0
Not directly comparable
LongBench v2
Not directly comparable
CharXiv
Kimi K3.1 Pro leads this result
CharXiv w/o tools
Kimi K3.1 Pro leads this result
Chartography (no tools)
Not directly comparable
Chartography (tools)
Kimi K3.1 Pro leads this result
BabyVision
Not directly comparable
BabyVision w/ Python
Kimi K3.1 Pro leads this result
ScreenSpot Pro
Not directly comparable
PerceptionBench
Kimi K3.1 Pro leads this result
MathVision
Not directly comparable
OfficeQA Pro
Not directly comparable
OfficeQA Pro v2
Not directly comparable
BenchCAD Vision2Code (no tools)
Not directly comparable
BenchCAD Vision2Code (tools)
Not directly comparable
MMMU-Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
ZeroBench
Not directly comparable
ZeroBench w/ Python
Not directly comparable
GDP.pdf (Gemini judge)
Not directly comparable
GDP.pdf w/ Python (Gemini judge)
Not directly comparable
GPQA
Kimi K3.1 Pro leads this result
GPQA-D
Kimi K3.1 Pro leads this result
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
Kimi Hallu Bench
Not directly comparable
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Last updated November 7, 2026
