Agentic
Directional only- Gemini 3.5 Flash-Lite
- 43.4
- Estimated · #104/151
- Ornith-1.0-9B
- 46.6
- Estimated · #85/151
- Basis
- BenchAlign lane · 3 vs 2 public rows
- Reading
- Directional only
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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
3 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.
Prompts that approach the documented context limit
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the larger documented context window.
Confidence: documented
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Ornith-1.0-9B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 3.5 Flash-Lite and Ornith-1.0-9B are scored on Estimated evidence for agentic, so the reading is 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: 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 rest 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 3.5 Flash-Lite | Ornith-1.0-9B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 43.4Estimated · #104/151 | 46.6Estimated · #85/151 | Directional onlyBenchAlign lane · 3 vs 2 public rows | Directional only |
| Coding | 43.6Supported · #121/183 | 46.7Estimated · #96/183 | Directional onlyBenchAlign lane · 4 vs 5 public rows | Directional only |
| Reasoning | 60.8Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 53.0Supported · #71/181 | Not ranked | Not comparableBenchAlign lane · 2 vs 0 public rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 76.1#16/48 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 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.
SWE-bench Pro
Coding
Terminal-Bench 2.0
Agentic
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
Ornith-1.0-9B has no comparable published API token rate.
50K fresh input + 3K output tokens
Ornith-1.0-9B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Ornith-1.0-9B 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 3.5 Flash-Lite
Ornith-1.0-9B
256K
Gemini 3.5 Flash-Lite
gemini-3.5-flash-lite
Google Gemini 3.5 Flash-Lite model documentationOrnith-1.0-9B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.5 Flash-Lite
$0.03 per 1M cached input tokens
Google Gemini API pricingOrnith-1.0-9B
No comparable hosted API rate
Gemini 3.5 Flash-Lite
text, image, video, audio, pdf
Google Gemini 3.5 Flash-Lite model documentationOrnith-1.0-9B
Not sourced
Gemini 3.5 Flash-Lite
Ornith-1.0-9B
Not sourced
Gemini 3.5 Flash-Lite
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideOrnith-1.0-9B
Not sourced
Gemini 3.5 Flash-Lite
Reasoning
Ornith-1.0-9B
Reasoning
Gemini 3.5 Flash-Lite
Proprietary
Ornith-1.0-9B
Open Weight
Gemini 3.5 Flash-Lite
Proprietary
Ornith-1.0-9B
Open Weight
Gemini 3.5 Flash-Lite
2026-07-21
Ornith-1.0-9B
2026-06-01
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
Gemini 3.5 Flash-Lite leads this result
OSWorld-Verified
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
Claw-Eval
Not directly comparable
Terminal-Bench 2.0
Gemini 3.5 Flash-Lite leads this result
SWE-bench Pro
Gemini 3.5 Flash-Lite leads this result
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE Multilingual
Not directly comparable
NL2Repo
Not directly comparable
MRCRv2
Not directly comparable
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Ornith-1.0-9B scores higher for coding on the public lane, 46.7 to 43.6. Ornith-1.0-9B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Ornith-1.0-9B scores higher for agentic tasks on the public lane, 46.6 to 43.4. Gemini 3.5 Flash-Lite and Ornith-1.0-9B are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Gemini 3.5 Flash-Lite has the larger documented context window: 1M, compared with 256K.
Last updated September 4, 2026
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