Agentic
Not comparable- Kimi K2.6
- 46.1
- Estimated · #88/151
- Trinity-Large-Preview
- Not ranked
- Basis
- BenchAlign lane · 12 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
Kimi K2.6 has the higher public score estimate, 65.3 versus 55.39, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 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
200K cached + 20K fresh input + 10K output tokens
Trinity-Large-Preview
Trinity-Large-Preview has the lower estimated token cost for this stated workload. Kimi K2.6 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
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
Trinity-Large-Preview is 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
Trinity-Large-Preview is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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 | Kimi K2.6 | Trinity-Large-Preview | Basis | Reading |
|---|---|---|---|---|
| Agentic | 46.1Estimated · #88/151 | Not ranked | Not comparableBenchAlign lane · 12 vs 0 public rows | Not comparable |
| Coding | 51.6Supported · #62/183 | Not ranked | Not comparableBenchAlign lane · 10 vs 0 public rows | Not comparable |
| Reasoning | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 61.7Supported · #34/181 | Not ranked | Not comparableBenchAlign lane · 5 vs 3 public rows | Not comparable |
| Math | 71.3#1/7 | Not ranked | Not comparableProvisional lane · 4 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 63.2#26/48 | Not ranked | Not comparableProvisional lane · 2 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
Trinity-Large-Preview has the lower modeled cost
Kimi K2.6 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.
Kimi K2.6
256K
Trinity-Large-Preview
512K
Kimi K2.6
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.
Kimi K2.6
Not published
Trinity-Large-Preview
Not published
Kimi K2.6
Not sourced
Trinity-Large-Preview
Not sourced
Kimi K2.6
Not sourced
Trinity-Large-Preview
Not sourced
Kimi K2.6
Not sourced
Trinity-Large-Preview
Not sourced
Kimi K2.6
Reasoning
Trinity-Large-Preview
Non-Reasoning
Kimi K2.6
Open Weight
Trinity-Large-Preview
Open Weight
Kimi K2.6
Open Weight
Trinity-Large-Preview
Open Weight
Kimi K2.6
2026-04-20
Trinity-Large-Preview
2026-01-27
Run the same representative tasks against both endpoints before changing production traffic.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Not directly comparable
OSWorld-Verified
Not directly comparable
Toolathlon
Not directly comparable
MCP Atlas
Not directly comparable
Claw-Eval
Not directly comparable
DeepSearchQA
Not directly comparable
WideResearch
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
OSWorld 2.0
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
SWE-bench Verified
Not directly comparable
LiveCodeBench v6
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
SciCode
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
cursorBench31
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Kimi K2.6 leads this result
HLE
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
MMLU
Not directly comparable
MMLU-Pro (Arcee)
Not directly comparable
AIME26
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
AIME25 (Arcee)
Not directly comparable
MMMU-Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
CharXiv
Not directly comparable
MathVision
Not directly comparable
V*
Not directly comparable
Kimi K2.6 has the higher public score estimate, 65.3 versus 55.39, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Trinity-Large-Preview is not ranked on the public lane for coding, so no winner is named for coding.
Trinity-Large-Preview is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
For the stated presets, chat costs $0.00295 on Kimi K2.6 and $0.00075 on Trinity-Large-Preview; repository review costs $0.0595 and $0.0155; the cache-heavy agent loop costs $0.249 and $0.065. Kimi K2.6 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 256K.
Last updated September 4, 2026
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