Agentic
Not comparable- DeepSeek V3
- 36.8
- Estimated · #130/152
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- BenchAlign lane · 0 vs 8 public rows
- Reading
- Not comparable
DeepSeek V3 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.
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
DeepSeek V3
DeepSeek V3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
DeepSeek V3
DeepSeek V3 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
Cache-heavy agent loop cost
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. DeepSeek V3 does not fit this workload in one request.
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.
5 DeepSeek V3 only
1 Shared
20 DeepSeek V4.1 Flash only
- Shared results
- 1
- DeepSeek V3 only
- 5
- DeepSeek V4.1 Flash only
- 20
- 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 V3
- 39.4
- Estimated · #120/151
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- BenchAlign lane · 2 vs 6 public rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek V3
- 41.2
- Unranked · 2 rankable rows
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V3
- 39.4
- Estimated · #138/182
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- BenchAlign lane · 2 vs 3 public rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V3
- 26.2
- Unranked · 1 rankable row
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V3
- Not ranked
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V3
- Not ranked
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V3
- 39.9
- #103/121
- DeepSeek V4.1 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V3 | DeepSeek V4.1 Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 36.8Estimated · #130/152 | Not ranked | Not comparableBenchAlign lane · 0 vs 8 public rows | Not comparable |
| Coding | 39.4Estimated · #120/151 | Not ranked | Not comparableBenchAlign lane · 2 vs 6 public rows | Not comparable |
| Reasoning | 41.2Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 39.4Estimated · #138/182 | Not ranked | Not comparableBenchAlign lane · 2 vs 3 public rows | Not comparable |
| Math | 26.2Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 1 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 | 39.9#103/121 | 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.
GPQA
Knowledge
DeepSeek V3: 59.1%DeepSeek V4.1 Flash: 90.9%Normalized gap 31.8DeepSeek V3 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
- DeepSeek V3
- $0.00082
- Fits in one request
- DeepSeek V4.1 Flash
- $0.0009
- Fits in one request
DeepSeek V3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- DeepSeek V3
- $0.0168
- Fits in one request
- DeepSeek V4.1 Flash
- $0.0186
- Fits in one request
DeepSeek V3 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 V3
- $0.0304
- Does not fit in one request
- DeepSeek V4.1 Flash
- $0.0192
- Fits in one request
DeepSeek V3 does not fit this workload in one request.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek V3DeepSeek V4.1 Flash
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V3
128K
DeepSeek V4.1 Flash
API model ID
DeepSeek V3
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.
DeepSeek V3
$0.07 per 1M cached input tokens
DeepSeek V4.1 Flash
$0.006 per 1M cached input tokens
DeepSeek: Models & PricingDocumented inputs
DeepSeek V3
Not sourced
DeepSeek V4.1 Flash
text, image
DeepSeek vision guideDocumented outputs
DeepSeek V3
Not sourced
DeepSeek V4.1 Flash
Provider availability
DeepSeek V3
Not sourced
DeepSeek V4.1 Flash
Generally Available · DeepSeek API, open weights
DeepSeek-V4.1-Flash releaseReasoning profile
DeepSeek V3
Non-Reasoning
DeepSeek V4.1 Flash
Reasoning
Weight access
DeepSeek V3
Open Weight
DeepSeek V4.1 Flash
Open Weight
License
DeepSeek V3
Open Weight
DeepSeek V4.1 Flash
Open Weight
Release date
DeepSeek V3
2024-12-26
DeepSeek V4.1 Flash
2026-09-10
If you already use one of these models
- Deployment change
- Both entries list DeepSeek as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- 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: $0.0168 vs $0.0186. Cache-heavy agent loop: $0.0304 vs $0.0192.
- Context tradeoff
- DeepSeek V4.1 Flash has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
DeepSeek V3
API / mo$1,028
Self-host / mo$18,221
Break-even1.2B/day
DeepSeek V4.1 Flash
API / mo$1,125
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence26 rows
Agentic
Terminal-Bench 2.1
DeepSeek V3—DeepSeek V4.1 Flash90.6%SourceNot directly comparable
terminalBench3
DeepSeek V3—DeepSeek V4.1 Flash30%SourceNot directly comparable
Terminal-Bench 4.0
DeepSeek V3—DeepSeek V4.1 Flash31.20%SourceNot directly comparable
CyberGym
DeepSeek V3—DeepSeek V4.1 Flash88.1%SourceNot directly comparable
ExploitGym
DeepSeek V3—DeepSeek V4.1 Flash15.3%SourceNot directly comparable
HLE w/ tools
DeepSeek V3—DeepSeek V4.1 Flash63.9%SourceNot directly comparable
AutomationBench
DeepSeek V3—DeepSeek V4.1 Flash54.8%SourceNot directly comparable
Agents' Last Exam
DeepSeek V3—DeepSeek V4.1 Flash31.8%SourceNot directly comparable
Coding
LiveCodeBench
DeepSeek V337.6%SourceDeepSeek V4.1 Flash—Not directly comparable
SWE-bench Verified
DeepSeek V342%SourceDeepSeek V4.1 Flash—Not directly comparable
Codeforces
DeepSeek V3—DeepSeek V4.1 Flash3471.0SourceNot directly comparable
Terminal-Bench 2.1
DeepSeek V3—DeepSeek V4.1 Flash90.6%SourceNot directly comparable
terminalBench3
DeepSeek V3—DeepSeek V4.1 Flash30%SourceNot directly comparable
DeepSWE
DeepSeek V3—DeepSeek V4.1 Flash74.2%SourceNot directly comparable
ProgramBench
DeepSeek V3—DeepSeek V4.1 Flash20.3%SourceNot directly comparable
NL2Repo
DeepSeek V3—DeepSeek V4.1 Flash65.4%SourceNot directly comparable
Knowledge
GPQA
DeepSeek V359.1%SourceDeepSeek V4.1 Flash90.9%SourceDeepSeek V4.1 Flash leads this result
MMLU-Pro
DeepSeek V375.9%SourceDeepSeek V4.1 Flash—Not directly comparable
GPQA-D
DeepSeek V3—DeepSeek V4.1 Flash90.9%SourceNot directly comparable
HLE
DeepSeek V3—DeepSeek V4.1 Flash36.8%SourceNot directly comparable
Math
Multimodal
Instruction following
IFEval
DeepSeek V386.1%SourceDeepSeek V4.1 Flash—Not directly comparable
Frequently asked questions
Which is better, DeepSeek V3 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, DeepSeek V3 or DeepSeek V4.1 Flash?
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 V3 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, DeepSeek V3 or DeepSeek V4.1 Flash?
For the stated presets, chat costs $0.00082 on DeepSeek V3 and $0.0009 on DeepSeek V4.1 Flash; repository review costs $0.0168 and $0.0186; the cache-heavy agent loop costs $0.0304 and $0.0192. DeepSeek V3 does not fit this workload in one request.
Which has the larger context window, DeepSeek V3 or DeepSeek V4.1 Flash?
DeepSeek V4.1 Flash has the larger documented context window: 1M, compared with 128K.
Related comparisons
Last updated September 10, 2026
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