Agentic
Not comparable- Agents-A1
- 50.7
- Estimated · #50/152
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- BenchAlign lane · 3 vs 8 public rows
- Reading
- Not comparable
Agents-A1 vs DeepSeek V4.1 Flash
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.
2 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
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Agents-A1 and DeepSeek V4.1 Flash are 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
Chat turn cost
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
Cache-heavy agent loop cost
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
Repository review cost
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 Agents-A1 only
2 Shared
19 DeepSeek V4.1 Flash only
- Shared results
- 2
- Agents-A1 only
- 4
- DeepSeek V4.1 Flash only
- 19
- 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- Agents-A1
- Not ranked
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- BenchAlign lane · 0 vs 6 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Agents-A1
- 34.1
- Unranked · 1 rankable row
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Agents-A1
- 50.8
- Estimated · #76/182
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- BenchAlign lane · 1 vs 3 public rows
- Reading
- Not comparable
Math
Not comparable- Agents-A1
- Not ranked
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Agents-A1
- Not ranked
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Agents-A1
- Not ranked
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Agents-A1
- Not ranked
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Agents-A1 | DeepSeek V4.1 Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 50.7Estimated · #50/152 | Not ranked | Not comparableBenchAlign lane · 3 vs 8 public rows | Not comparable |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign lane · 0 vs 6 public rows | Not comparable |
| Reasoning | 34.1Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 50.8Estimated · #76/182 | Not ranked | Not comparableBenchAlign lane · 1 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.
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.
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.
HLE
Knowledge
Agents-A1: 47.6%DeepSeek V4.1 Flash: 36.8%Normalized gap 10.8Agents-A1 source DeepSeek V4.1 Flash source
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
- Agents-A1
- API rate not published
- Fits in one request
- DeepSeek V4.1 Flash
- $0.0009
- Fits in one request
Agents-A1 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Agents-A1
- API rate not published
- Fits in one request
- DeepSeek V4.1 Flash
- $0.0186
- Fits in one request
Agents-A1 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Agents-A1
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- DeepSeek V4.1 Flash
- $0.0192
- Fits in one request
Agents-A1 has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationAgents-A1DeepSeek V4.1 Flash
Context window
Maximum documented context; output-token limits may be lower.
Agents-A1
262K
DeepSeek V4.1 Flash
API model ID
Agents-A1
Not sourced
DeepSeek V4.1 Flash
deepseek-flash
DeepSeek models and pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Agents-A1
No comparable hosted API rate
DeepSeek V4.1 Flash
$0.006 per 1M cached input tokens
DeepSeek: Models & PricingDocumented inputs
Agents-A1
Not sourced
DeepSeek V4.1 Flash
text, image
DeepSeek vision guideDocumented outputs
Agents-A1
Not sourced
DeepSeek V4.1 Flash
Provider availability
Agents-A1
Not sourced
DeepSeek V4.1 Flash
Generally Available · DeepSeek API, open weights
DeepSeek-V4.1-Flash releaseReasoning profile
Agents-A1
Reasoning
DeepSeek V4.1 Flash
Reasoning
Weight access
Agents-A1
Open Weight
DeepSeek V4.1 Flash
Open Weight
License
Agents-A1
Open Weight
DeepSeek V4.1 Flash
Open Weight
Release date
Agents-A1
2026-06-26
DeepSeek V4.1 Flash
2026-09-10
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
- A complete comparable API-rate estimate is not available for both models.
- 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
BrowseComp
Agents-A175.5%SourceDeepSeek V4.1 Flash—Not directly comparable
HLE w/ tools
Agents-A147.6%SourceDeepSeek V4.1 Flash63.9%SourceDeepSeek V4.1 Flash leads this result
VITA-Bench
Agents-A138.8%SourceDeepSeek V4.1 Flash—Not directly comparable
Terminal-Bench 2.1
Agents-A1—DeepSeek V4.1 Flash90.6%SourceNot directly comparable
terminalBench3
Agents-A1—DeepSeek V4.1 Flash30%SourceNot directly comparable
Terminal-Bench 4.0
Agents-A1—DeepSeek V4.1 Flash31.20%SourceNot directly comparable
CyberGym
Agents-A1—DeepSeek V4.1 Flash88.1%SourceNot directly comparable
ExploitGym
Agents-A1—DeepSeek V4.1 Flash15.3%SourceNot directly comparable
AutomationBench
Agents-A1—DeepSeek V4.1 Flash54.8%SourceNot directly comparable
Agents' Last Exam
Agents-A1—DeepSeek V4.1 Flash31.8%SourceNot directly comparable
Coding
Codeforces
Agents-A1—DeepSeek V4.1 Flash3471.0SourceNot directly comparable
Terminal-Bench 2.1
Agents-A1—DeepSeek V4.1 Flash90.6%SourceNot directly comparable
terminalBench3
Agents-A1—DeepSeek V4.1 Flash30%SourceNot directly comparable
DeepSWE
Agents-A1—DeepSeek V4.1 Flash74.2%SourceNot directly comparable
ProgramBench
Agents-A1—DeepSeek V4.1 Flash20.3%SourceNot directly comparable
NL2Repo
Agents-A1—DeepSeek V4.1 Flash65.4%SourceNot directly comparable
Reasoning
LongBench v2
Agents-A160.2%SourceDeepSeek V4.1 Flash—Not directly comparable
Knowledge
Math
Apex
Agents-A1—DeepSeek V4.1 Flash65.6%SourceNot directly comparable
Multimodal
Instruction following
IFEval
Agents-A194.8%SourceDeepSeek V4.1 Flash—Not directly comparable
Frequently asked questions
Which is better, Agents-A1 or DeepSeek V4.1 Flash?
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, Agents-A1 or DeepSeek V4.1 Flash?
Agents-A1 and DeepSeek V4.1 Flash are not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Agents-A1 or DeepSeek V4.1 Flash?
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, Agents-A1 or DeepSeek V4.1 Flash?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, Agents-A1 or DeepSeek V4.1 Flash?
DeepSeek V4.1 Flash has the larger documented context window: 1M, compared with 262K.
Related comparisons
Last updated September 10, 2026
Watch Agents-A1 vs DeepSeek V4.1 Flash
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.