Agentic
Not comparable- o3-pro
- Not ranked
- Trinity-Large-Preview
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 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
Trinity-Large-Preview
Trinity-Large-Preview has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
Trinity-Large-Preview
Trinity-Large-Preview has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
50K fresh input + 3K output tokens
Trinity-Large-Preview
Trinity-Large-Preview has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Code generation, repair, and software-engineering tasks
Not enough matched evidence
O3-pro and Trinity-Large-Preview are not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
O3-pro and Trinity-Large-Preview are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. o3-pro does not fit this workload in one request. o3-pro has no published cached-input rate, so cached tokens use its listed input rate. Trinity-Large-Preview has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
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 | o3-pro | Trinity-Large-Preview | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Reasoning | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 50.2Estimated · #89/181 | Not ranked | Not comparableBenchAlign lane · 0 vs 3 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 | Not ranked | 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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
Trinity-Large-Preview has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Trinity-Large-Preview has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
o3-pro does not fit this workload in one request. o3-pro has no published cached-input rate, so cached tokens use its listed input rate. Trinity-Large-Preview has no published cached-input rate, so cached tokens use its listed input 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.
o3-pro
200K
Trinity-Large-Preview
512K
o3-pro
Not sourced
Trinity-Large-Preview
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
o3-pro
Not published
Trinity-Large-Preview
Not published
o3-pro
Not sourced
Trinity-Large-Preview
Not sourced
o3-pro
Not sourced
Trinity-Large-Preview
Not sourced
o3-pro
Not sourced
Trinity-Large-Preview
Not sourced
o3-pro
Reasoning
Trinity-Large-Preview
Non-Reasoning
o3-pro
Proprietary
Trinity-Large-Preview
Open Weight
o3-pro
Proprietary
Trinity-Large-Preview
Open Weight
o3-pro
2025-04-16
Trinity-Large-Preview
2026-01-27
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.
AIME25 (Arcee)
Not directly comparable
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
O3-pro and Trinity-Large-Preview are not ranked on the public lane for coding, so no winner is named for coding.
O3-pro and Trinity-Large-Preview are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
For the stated presets, chat costs $0.06 on o3-pro and $0.00075 on Trinity-Large-Preview; repository review costs $1.24 and $0.0155; the cache-heavy agent loop costs $5.20 and $0.065. o3-pro does not fit this workload in one request. o3-pro has no published cached-input rate, so cached tokens use its listed input rate. Trinity-Large-Preview has no published cached-input rate, so cached tokens use its listed input rate.
Trinity-Large-Preview has the larger documented context window: 512K, compared with 200K.
Last updated September 4, 2026
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.