Agentic
Like-for-like- DeepSeek V4 Pro (High)
- 70.6
- Nemotron 3.5 Lightning 30B A3B NVFP4
- 29.1
- Weighted basis
- 2 vs 2 rows
- Reading
- DeepSeek V4 Pro (High) 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
DeepSeek V4 Pro (High) has the higher public score, 55.54 versus 26.93, and the 90% score intervals do not overlap.
8 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
DeepSeek V4 Pro (High)
DeepSeek V4 Pro (High) leads on the same 2 weighted benchmark rows.
Confidence: limited
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
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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.
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 | DeepSeek V4 Pro (High) | Nemotron 3.5 Lightning 30B A3B NVFP4 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 70.6 | 29.1 | Like-for-like2 vs 2 rows | DeepSeek V4 Pro (High) leads |
| Coding | 69.8 | 42.1 | Directional only2 vs 2 rows | Directional only |
| Knowledge | 57.0 | 80.5 | Directional only4 vs 2 rows | Directional only |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | 94.0 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 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.
BrowseComp
Agentic
Terminal-Bench 2.0
Agentic
SWE-bench Verified
Coding
GPQA
Knowledge
MMLU-Pro
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.
DeepSeek V4 Pro (High)
Nemotron 3.5 Lightning 30B A3B NVFP4
DeepSeek V4 Pro (High)
deepseek-v4-pro
DeepSeek models and pricingNemotron 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.
DeepSeek V4 Pro (High)
$0.003625 per 1M cached input tokens
Nemotron 3.5 Lightning 30B A3B NVFP4
No comparable hosted API rate
NVIDIA Nemotron 3.5 Lightning model cardDeepSeek V4 Pro (High)
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
DeepSeek V4 Pro (High)
Nemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
DeepSeek V4 Pro (High)
Preview · DeepSeek API, open weights
DeepSeek V4 Flash 0731 updateNemotron 3.5 Lightning 30B A3B NVFP4
Not sourced
DeepSeek V4 Pro (High)
Reasoning
Nemotron 3.5 Lightning 30B A3B NVFP4
Reasoning
DeepSeek V4 Pro (High)
Open Weight
Nemotron 3.5 Lightning 30B A3B NVFP4
Open Weight
DeepSeek V4 Pro (High)
Open Weight
Nemotron 3.5 Lightning 30B A3B NVFP4
Open Weight
DeepSeek V4 Pro (High)
2026-04-24
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
DeepSeek V4 Pro (High) leads this result
BrowseComp
DeepSeek V4 Pro (High) leads this result
HLE w/ tools
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
PinchBench
Not directly comparable
τ³-bench results
Not directly comparable
LiveCodeBench Pass@1-COT
Not directly comparable
Codeforces
Not directly comparable
SWE-bench Verified
DeepSeek V4 Pro (High) leads this result
SWE-bench Pro
Not directly comparable
SWE Multilingual
DeepSeek V4 Pro (High) leads this result
Terminal-Bench 2.0
DeepSeek V4 Pro (High) leads this result
SciCode
Not directly comparable
MMLU-Pro
DeepSeek V4 Pro (High) leads this result
SimpleQA
Not directly comparable
Chinese-SimpleQA
Not directly comparable
GPQA
DeepSeek V4 Pro (High) leads this result
GPQA-D
DeepSeek V4 Pro (High) leads this result
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
HMMT Feb 2026
Not directly comparable
IMOAnswerBench
Not directly comparable
Apex
Not directly comparable
Apex Shortlist
Not directly comparable
IFBench
Not directly comparable
DeepSeek V4 Pro (High) has the higher public score, 55.54 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.
DeepSeek V4 Pro (High) 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.
Both models list the same context window, 1M.
Last updated August 11, 2026
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