Agentic
Like-for-like- Kimi K3
- 89.5
- Nemotron 3.5 Lightning 30B A3B NVFP4
- 29.1
- Weighted basis
- 2 vs 2 rows
- Reading
- Kimi K3 leads
Provider changes are easy to miss. Radar watches releases, pricing, deprecations, and incidents at the source.Provider changes are easy to miss.
See RadarModel comparison
Updated August 11, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
NVIDIA
26.9/100
Estimated · Public rank #204
90% interval 17.1–36.8
Kimi K3 has the higher public score, 80.48 versus 26.93, and the 90% score intervals do not overlap.
5 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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
Kimi K3 leads on the same 2 weighted benchmark rows.
Confidence: limited
Prompts that approach the documented context limit
Kimi K3
Kimi K3 has the larger documented context window.
Confidence: documented
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
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
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-rates
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
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
| Category | Kimi K3 | Nemotron 3.5 Lightning 30B A3B NVFP4 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 89.5 | 29.1 | Like-for-like2 vs 2 rows | Kimi K3 leads |
| Knowledge | 61.0 | 80.5 | Directional only2 vs 2 rows | Directional only |
| Coding | Not measured | 42.1 | Not comparable0 vs 2 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 78.5 | Not measured | Not comparable3 vs 0 rows | Not comparable |
| Instruction following | Not measured | 72.9 | Not comparable0 vs 1 rows | Not comparable |
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.
Terminal-Bench 2.0
Agentic
BrowseComp
Agentic
GPQA
Knowledge
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
Nemotron 3.5 Lightning 30B A3B NVFP4 has no comparable published API token rate.
50K fresh input + 3K output tokens
Nemotron 3.5 Lightning 30B A3B NVFP4 has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Nemotron 3.5 Lightning 30B A3B NVFP4 has no comparable published API token rate.
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.
Kimi K3
1.05M
Nemotron 3.5 Lightning 30B A3B NVFP4
Kimi K3
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Kimi K3
$0.3 per 1M cached input tokens
Nemotron 3.5 Lightning 30B A3B NVFP4
No comparable hosted API rate
NVIDIA Nemotron 3.5 Lightning model cardKimi K3
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
Kimi K3
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
Kimi K3
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
Kimi K3
Reasoning
Nemotron 3.5 Lightning 30B A3B NVFP4
Reasoning
Kimi K3
Pending
Nemotron 3.5 Lightning 30B A3B NVFP4
Open Weight
Kimi K3
Pending
Nemotron 3.5 Lightning 30B A3B NVFP4
Open Weight
Kimi K3
2026-07-16
Nemotron 3.5 Lightning 30B A3B NVFP4
2026-08-11
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Kimi K3 leads this result
BrowseComp
Kimi K3 leads this result
DeepSearchQA
Not directly comparable
Toolathlon-Verified
Not directly comparable
MCP Atlas
Not directly comparable
AutomationBench
Not directly comparable
JobBench
Not directly comparable
APEX-Agents
Not directly comparable
SpreadsheetBench 2
Not directly comparable
DECK-Bench
Not directly comparable
PinchBench
Not directly comparable
τ³-bench results
Not directly comparable
deepSwe
Not directly comparable
FrontierSWE
Not directly comparable
ProgramBench
Not directly comparable
Kimi Code Bench v2
Not directly comparable
sweMarathon
Not directly comparable
PostTrain Bench
Not directly comparable
MLS-Bench Lite
Not directly comparable
VulcanBench v3
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE Multilingual
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
SciCode
Not directly comparable
GPQA
Kimi K3 leads this result
GPQA-D
Kimi K3 leads this result
HLE
Not directly comparable
HLE w/o tools
Kimi K3 leads this result
MMLU-Pro
Not directly comparable
OfficeQA Pro
Not directly comparable
MMMU-Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
CharXiv w/o tools
Not directly comparable
CharXiv
Not directly comparable
MathVision
Not directly comparable
MathVision w/ Python
Not directly comparable
BabyVision w/ Python
Not directly comparable
ZeroBench
Not directly comparable
ZeroBench w/ Python
Not directly comparable
WorldVQA ForceAnswer
Not directly comparable
OmniDocBench
Not directly comparable
PerceptionBench
Not directly comparable
IFBench
Not directly comparable
Kimi K3 has the higher public score, 80.48 versus 26.93, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Kimi K3 leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Kimi K3 has the larger documented context window: 1.05M, compared with 1M.
Last updated August 11, 2026
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