Agentic
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- Trinity-Large-Thinking
- 38.2
- Estimated · #123/152
- Basis
- BenchAlign lane · 8 vs 1 public rows
- Reading
- Not comparable
DeepSeek V4.1 Flash vs Trinity-Large-Thinking
Updated September 10, 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.
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
Which one for your work
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.
Long documents
Prompts that approach the documented context limit
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Trinity-Large-Thinking
Trinity-Large-Thinking has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash has the lower estimated token cost for this stated workload. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Trinity-Large-Thinking
Trinity-Large-Thinking has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
DeepSeek V4.1 Flash is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
DeepSeek V4.1 Flash is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
20 DeepSeek V4.1 Flash only
1 Shared
4 Trinity-Large-Thinking only
- Shared results
- 1
- DeepSeek V4.1 Flash only
- 20
- Trinity-Large-Thinking only
- 4
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
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.
Coding
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- Trinity-Large-Thinking
- 27.4
- Supported · #143/151
- Basis
- BenchAlign lane · 6 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- Trinity-Large-Thinking
- 47.1
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- Trinity-Large-Thinking
- 42.1
- Estimated · #124/182
- Basis
- BenchAlign lane · 3 vs 2 public rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- Trinity-Large-Thinking
- 67.8
- #65/121
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V4.1 Flash | Trinity-Large-Thinking | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 38.2Estimated · #123/152 | Not comparableBenchAlign lane · 8 vs 1 public rows | Not comparable |
| Coding | Not ranked | 27.4Supported · #143/151 | Not comparableBenchAlign lane · 6 vs 1 public rows | Not comparable |
| Reasoning | Not ranked | 47.1Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 42.1Estimated · #124/182 | Not comparableBenchAlign lane · 3 vs 2 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 | 67.8#65/121 | 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.
Shape of the matched evidence
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.
What each workload costs
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.
Chat turn
1K fresh input + 500 output tokens
- DeepSeek V4.1 Flash
- $0.0009
- Fits in one request
- Trinity-Large-Thinking
- $0.0007
- Fits in one request
Trinity-Large-Thinking has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- DeepSeek V4.1 Flash
- $0.0186
- Fits in one request
- Trinity-Large-Thinking
- $0.0152
- Fits in one request
Trinity-Large-Thinking has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- DeepSeek V4.1 Flash
- $0.0192
- Fits in one request
- Trinity-Large-Thinking
- $0.064
- Fits in one request
- Cached input priced at the published list-input rate
DeepSeek V4.1 Flash has the lower modeled cost
Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek V4.1 FlashTrinity-Large-Thinking
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V4.1 Flash
Trinity-Large-Thinking
512K
API model ID
DeepSeek V4.1 Flash
deepseek-flash
DeepSeek models and pricingTrinity-Large-Thinking
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek V4.1 Flash
$0.006 per 1M cached input tokens
DeepSeek: Models & PricingTrinity-Large-Thinking
Not published
Documented inputs
DeepSeek V4.1 Flash
text, image
DeepSeek vision guideTrinity-Large-Thinking
Not sourced
Documented outputs
DeepSeek V4.1 Flash
Trinity-Large-Thinking
Not sourced
Provider availability
DeepSeek V4.1 Flash
Generally Available · DeepSeek API, open weights
DeepSeek-V4.1-Flash releaseTrinity-Large-Thinking
Not sourced
Reasoning profile
DeepSeek V4.1 Flash
Reasoning
Trinity-Large-Thinking
Reasoning
Weight access
DeepSeek V4.1 Flash
Open Weight
Trinity-Large-Thinking
Open Weight
License
DeepSeek V4.1 Flash
Open Weight
Trinity-Large-Thinking
Open Weight
Release date
DeepSeek V4.1 Flash
2026-09-10
Trinity-Large-Thinking
2026-03-10
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.0186 vs $0.0152. Cache-heavy agent loop: $0.0192 vs $0.064.
- Context tradeoff
- DeepSeek V4.1 Flash has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence25 rows
Agentic
Terminal-Bench 2.1
DeepSeek V4.1 Flash90.6%SourceTrinity-Large-Thinking—Not directly comparable
terminalBench3
DeepSeek V4.1 Flash30%SourceTrinity-Large-Thinking—Not directly comparable
Terminal-Bench 4.0
DeepSeek V4.1 Flash31.20%SourceTrinity-Large-Thinking—Not directly comparable
CyberGym
DeepSeek V4.1 Flash88.1%SourceTrinity-Large-Thinking—Not directly comparable
ExploitGym
DeepSeek V4.1 Flash15.3%SourceTrinity-Large-Thinking—Not directly comparable
HLE w/ tools
DeepSeek V4.1 Flash63.9%SourceTrinity-Large-Thinking—Not directly comparable
AutomationBench
DeepSeek V4.1 Flash54.8%SourceTrinity-Large-Thinking—Not directly comparable
Agents' Last Exam
DeepSeek V4.1 Flash31.8%SourceTrinity-Large-Thinking—Not directly comparable
Gert Labs
DeepSeek V4.1 Flash—Trinity-Large-Thinking32.55%SourceNot directly comparable
Coding
Codeforces
DeepSeek V4.1 Flash3471.0SourceTrinity-Large-Thinking—Not directly comparable
Terminal-Bench 2.1
DeepSeek V4.1 Flash90.6%SourceTrinity-Large-Thinking—Not directly comparable
terminalBench3
DeepSeek V4.1 Flash30%SourceTrinity-Large-Thinking—Not directly comparable
DeepSWE
DeepSeek V4.1 Flash74.2%SourceTrinity-Large-Thinking—Not directly comparable
ProgramBench
DeepSeek V4.1 Flash20.3%SourceTrinity-Large-Thinking—Not directly comparable
NL2Repo
DeepSeek V4.1 Flash65.4%SourceTrinity-Large-Thinking—Not directly comparable
SWE-bench Verified*
DeepSeek V4.1 Flash—Trinity-Large-Thinking63.2%SourceNot directly comparable
Knowledge
GPQA
DeepSeek V4.1 Flash90.9%SourceTrinity-Large-Thinking—Not directly comparable
GPQA-D
DeepSeek V4.1 Flash90.9%SourceTrinity-Large-Thinking76.3%SourceDeepSeek V4.1 Flash leads this result
HLE
DeepSeek V4.1 Flash36.8%SourceTrinity-Large-Thinking—Not directly comparable
MMLU-Pro (Arcee)
DeepSeek V4.1 Flash—Trinity-Large-Thinking83.4%SourceNot directly comparable
Math
Multimodal
Frequently asked questions
Which is better, DeepSeek V4.1 Flash or Trinity-Large-Thinking?
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.
Which is better for coding, DeepSeek V4.1 Flash or Trinity-Large-Thinking?
DeepSeek V4.1 Flash is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, DeepSeek V4.1 Flash or Trinity-Large-Thinking?
DeepSeek V4.1 Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, DeepSeek V4.1 Flash or Trinity-Large-Thinking?
For the stated presets, chat costs $0.0009 on DeepSeek V4.1 Flash and $0.0007 on Trinity-Large-Thinking; repository review costs $0.0186 and $0.0152; the cache-heavy agent loop costs $0.0192 and $0.064. Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, DeepSeek V4.1 Flash or Trinity-Large-Thinking?
DeepSeek V4.1 Flash has the larger documented context window: 1M, compared with 512K.
Related comparisons
Last updated September 10, 2026
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