Agentic
Like-for-like- Kimi K2.5
- 55.0
- Nemotron 3.5 Lightning 30B A3B NVFP4
- 29.1
- Weighted basis
- 2 vs 2 rows
- Reading
- Kimi K2.5 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 K2.5 has the higher public score, 58.8 versus 26.93, and the 90% score intervals do not overlap.
9 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 K2.5
Kimi K2.5 leads on the same 2 weighted benchmark rows.
Confidence: limited
Prompts that approach the documented context limit
Nemotron 3.5 Lightning 30B A3B NVFP4
Nemotron 3.5 Lightning 30B A3B NVFP4 has the larger documented context window.
Confidence: documented
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
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: rate-fallback
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.
2 categories use 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 K2.5 | Nemotron 3.5 Lightning 30B A3B NVFP4 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 55.0 | 29.1 | Like-for-like2 vs 2 rows | Kimi K2.5 leads |
| Coding | 59.4 | 42.1 | Directional only4 vs 2 rows | Directional only |
| Knowledge | 56.9 | 80.5 | Directional only4 vs 2 rows | Directional only |
| Reasoning | 61.0 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 60.6 | Not measured | Not comparable4 vs 0 rows | Not comparable |
| Multilingual | 82.3 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multimodal | 78.5 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Instruction following | 93.9 | 72.9 | Not comparable1 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
SWE-bench Verified
Coding
BrowseComp
Agentic
SciCode
Coding
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
Kimi K2.5 has no published cached-input rate, so cached tokens use its listed input rate. 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 K2.5
256K
Nemotron 3.5 Lightning 30B A3B NVFP4
Kimi K2.5
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 K2.5
Not published
Nemotron 3.5 Lightning 30B A3B NVFP4
No comparable hosted API rate
NVIDIA Nemotron 3.5 Lightning model cardKimi K2.5
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
Kimi K2.5
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
Kimi K2.5
Not sourced
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
Kimi K2.5
Non-Reasoning
Nemotron 3.5 Lightning 30B A3B NVFP4
Reasoning
Kimi K2.5
Open Weight
Nemotron 3.5 Lightning 30B A3B NVFP4
Open Weight
Kimi K2.5
Open Weight
Nemotron 3.5 Lightning 30B A3B NVFP4
Open Weight
Kimi K2.5
2026-02-01
Nemotron 3.5 Lightning 30B A3B NVFP4
2026-08-11
Run the same representative tasks against both endpoints before changing production traffic.
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 2.0
Kimi K2.5 leads this result
BrowseComp
Kimi K2.5 leads this result
Claw-Eval
Not directly comparable
QwenClawBench
Not directly comparable
τ³-bench results
Kimi K2.5 leads this result
DeepSearchQA
Not directly comparable
DeepPlanning
Not directly comparable
Toolathlon
Not directly comparable
MCP Atlas
Not directly comparable
MCP-Tasks
Not directly comparable
WideResearch
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
JobBench
Not directly comparable
PinchBench
Not directly comparable
SWE-bench Verified
Kimi K2.5 leads this result
SWE-bench Verified*
Not directly comparable
LiveCodeBench v6
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Kimi K2.5 leads this result
SWE-Rebench
Not directly comparable
React Native Evals
Not directly comparable
SciCode
Kimi K2.5 leads this result
Terminal-Bench 2.0
Not directly comparable
LongBench v2
Not directly comparable
GPQA
Kimi K2.5 leads this result
GPQA-D
Kimi K2.5 leads this result
SuperGPQA
Not directly comparable
MMLU-Pro
Kimi K2.5 leads this result
MMLU-Pro (Arcee)
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
AIME 2025
Not directly comparable
AIME26
Not directly comparable
AIME25 (Arcee)
Not directly comparable
HMMT Feb 2025
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
MMMU-Pro
Not directly comparable
Video-MME
Not directly comparable
MMVU
Not directly comparable
VideoMMMU
Not directly comparable
Kimi K2.5 has the higher public score, 58.8 versus 26.93, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Kimi K2.5 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.
Nemotron 3.5 Lightning 30B A3B NVFP4 has the larger documented context window: 1M, compared with 256K.
Last updated August 11, 2026
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