Agentic
Like-for-like- Inkling-Small
- 36.9
- Supported · #128/152
- Nemotron 3 Ultra
- 27.0
- Supported · #143/152
- Basis
- BenchAlign lane · 5 vs 6 public rows
- Reading
- Inkling-Small leads · intervals overlap
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Follow model changesUpdated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Inkling-Small has the higher public score, 59.25 versus 40.51, and the 90% score intervals do not overlap.
16 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
Inkling-Small
Inkling-Small leads on the public coding lane, 43.8 to 26.6, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Tool use, computer use, and multi-step task completion
Inkling-Small
Inkling-Small leads on the public agentic lane, 36.9 to 27, with Supported evidence for both models, although the 90% intervals overlap.
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.
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 | Inkling-Small | Nemotron 3 Ultra | Basis | Reading |
|---|---|---|---|---|
| Agentic | 36.9Supported · #128/152 | 27.0Supported · #143/152 | Like-for-likeBenchAlign lane · 5 vs 6 public rows | Inkling-Small leads · intervals overlap |
| Coding | 43.8Supported · #97/151 | 26.6Supported · #145/151 | Like-for-likeBenchAlign lane · 6 vs 7 public rows | Inkling-Small leads · intervals overlap |
| Instruction following | 89.6#25/123 | 88.8#28/123 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Inkling-Small leads |
| Knowledge | 56.5Supported · #46/183 | 44.7Estimated · #111/183 | Directional onlyBenchAlign lane · 6 vs 7 public rows | Directional only |
| Reasoning | 42.7Unranked · 3 rankable rows | 48.7Unranked · 1 rankable row | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Math | 76.9Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | 47.4#7/12 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | 48.8#39/48 | Not ranked | Not comparableProvisional lane · 2 vs 0 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.
BrowseComp
Agentic
HLE
Knowledge
Terminal-Bench 2.0
Agentic
SWE-bench Verified
Coding
HLE w/o tools
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 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.
Inkling-Small
1M
Nemotron 3 Ultra
1M
Inkling-Small
Not sourced
Nemotron 3 Ultra
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Inkling-Small
$0.116 per 1M cached input tokens
Nemotron 3 Ultra
No comparable hosted API rate
Inkling-Small
Not sourced
Nemotron 3 Ultra
Not sourced
Inkling-Small
Not sourced
Nemotron 3 Ultra
Not sourced
Inkling-Small
Not sourced
Nemotron 3 Ultra
Not sourced
Inkling-Small
Hybrid
Nemotron 3 Ultra
Reasoning
Inkling-Small
Open Weight
Nemotron 3 Ultra
Open Weight
Inkling-Small
Open Weight
Nemotron 3 Ultra
Open Weight
Inkling-Small
2026-07-30
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.
Terminal-Bench 2.0
Inkling-Small leads this result
BrowseComp
Inkling-Small leads this result
MCP Atlas
Not directly comparable
Toolathlon-Verified
Not directly comparable
Terminal-Bench 2.1 (Vals)
Inkling-Small leads this result
PinchBench
Not directly comparable
τ³-bench results
Not directly comparable
HLE w/ tools
Not directly comparable
SWE-bench Verified
Inkling-Small leads this result
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.0
Inkling-Small leads this result
SciCode
Inkling-Small leads this result
LiveCodeBench (Vals)
Nemotron 3 Ultra leads this result
SWE-bench (Vals)
Inkling-Small leads this result
SWE Multilingual
Not directly comparable
LiveCodeBench v6
Not directly comparable
GPQA
Inkling-Small leads this result
GPQA-D
Inkling-Small leads this result
HLE
Inkling-Small leads this result
HLE w/o tools
Inkling-Small leads this result
GPQA Diamond (Vals)
Nemotron 3 Ultra leads this result
MMLU-Pro (Vals)
Nemotron 3 Ultra leads this result
MMLU-Pro
Not directly comparable
MMLU-ProX
Not directly comparable
Inkling-Small has the higher public score, 59.25 versus 40.51, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Inkling-Small leads the public coding lane, 43.8 to 26.6, with Supported evidence for both models, although the 90% intervals overlap.
Inkling-Small leads the public agentic tasks lane, 36.9 to 27, with Supported evidence for both models, although the 90% intervals overlap.
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 10, 2026
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