Agentic
Like-for-like- Gemini 3.6 Flash
- 50.7
- Supported · #60/151
- Nemotron 3 Ultra
- 22.3
- Supported · #150/151
- Basis
- BenchAlign lane · 2 vs 6 public rows
- Reading
- Gemini 3.6 Flash leads
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
See the free Radar BriefUpdated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Gemini 3.6 Flash has the higher public score, 70.11 versus 40.69, and the 90% score intervals do not overlap.
5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Code generation, repair, and software-engineering tasks
Gemini 3.6 Flash
Gemini 3.6 Flash leads on the public coding lane, 58.9 to 26.9, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Tool use, computer use, and multi-step task completion
Gemini 3.6 Flash
Gemini 3.6 Flash leads on the public agentic lane, 50.7 to 22.3, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
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.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Each row shows the public-lane category score for both models: the BenchAlign 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 | Gemini 3.6 Flash | Nemotron 3 Ultra | Basis | Reading |
|---|---|---|---|---|
| Agentic | 50.7Supported · #60/151 | 22.3Supported · #150/151 | Like-for-likeBenchAlign lane · 2 vs 6 public rows | Gemini 3.6 Flash leads |
| Coding | 58.9Supported · #30/183 | 26.9Supported · #174/183 | Like-for-likeBenchAlign lane · 4 vs 7 public rows | Gemini 3.6 Flash leads |
| Knowledge | 68.6Supported · #18/181 | 45.7Estimated · #114/181 | Directional onlyBenchAlign lane · 2 vs 7 public rows | Directional only |
| Reasoning | 77.8Unranked · 2 rankable rows | 48.7Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | 47.4#7/12 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | 82.3Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 88.7#28/120 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
MMLU-Pro (Vals)
Knowledge
LiveCodeBench (Vals)
Coding
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 Ultra has no comparable published API token rate.
50K fresh input + 3K output tokens
Nemotron 3 Ultra has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Nemotron 3 Ultra 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.
Gemini 3.6 Flash
Nemotron 3 Ultra
1M
Gemini 3.6 Flash
gemini-3.6-flash
Google Gemini 3.6 Flash model documentationNemotron 3 Ultra
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.6 Flash
$0.15 per 1M cached input tokens
Google Gemini API pricingNemotron 3 Ultra
No comparable hosted API rate
Gemini 3.6 Flash
text, image, video, audio, pdf
Google Gemini 3.6 Flash model documentationNemotron 3 Ultra
Not sourced
Gemini 3.6 Flash
Nemotron 3 Ultra
Not sourced
Gemini 3.6 Flash
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideNemotron 3 Ultra
Not sourced
Gemini 3.6 Flash
Reasoning
Nemotron 3 Ultra
Reasoning
Gemini 3.6 Flash
Proprietary
Nemotron 3 Ultra
Open Weight
Gemini 3.6 Flash
Proprietary
Nemotron 3 Ultra
Open Weight
Gemini 3.6 Flash
2026-07-21
Nemotron 3 Ultra
2026-06-04
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.
OSWorld-Verified
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.6 Flash leads this result
Terminal-Bench 2.0
Not directly comparable
PinchBench
Not directly comparable
BrowseComp
Not directly comparable
τ³-bench results
Not directly comparable
HLE w/ tools
Not directly comparable
deepSwe
Not directly comparable
cursorBench32
Not directly comparable
LiveCodeBench (Vals)
Gemini 3.6 Flash leads this result
SWE-bench (Vals)
Gemini 3.6 Flash leads this result
SWE-bench Verified
Not directly comparable
SWE Multilingual
Not directly comparable
LiveCodeBench v6
Not directly comparable
SciCode
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
GPQA Diamond (Vals)
Gemini 3.6 Flash leads this result
MMLU-Pro (Vals)
Gemini 3.6 Flash leads this result
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
MMLU-Pro
Not directly comparable
MMLU-ProX
Not directly comparable
IFBench
Not directly comparable
Gemini 3.6 Flash has the higher public score, 70.11 versus 40.69, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Gemini 3.6 Flash leads the public coding lane, 58.9 to 26.9, with Supported evidence for both models and non-overlapping 90% intervals.
Gemini 3.6 Flash leads the public agentic tasks lane, 50.7 to 22.3, with Supported evidence for both models and non-overlapping 90% intervals.
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 September 4, 2026
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