Agentic
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- GPT-6 Sol
- 69.7
- Estimated · #7/157
- Basis
- BenchAlign lane · 9 vs 4 public rows
- Reading
- Not comparable
DeepSeek V4.1 Flash vs GPT-6 Sol
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.
4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Updated September 22, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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
GPT-6 Sol
GPT-6 Sol has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash 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. 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 V4.1 Flash
DeepSeek V4.1 Flash 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
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
—DeepSeek V4.1 Flash74.3GPT-6 Sol
Not comparable · BenchAlign
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
18 DeepSeek V4.1 Flash only
4 Shared
6 GPT-6 Sol only
- Shared results
- 4
- DeepSeek V4.1 Flash only
- 18
- GPT-6 Sol only
- 6
- 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
- GPT-6 Sol
- 74.3
- Estimated · #6/159
- Basis
- BenchAlign lane · 6 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek V4.1 Flash
- 78.5
- Unranked · 2 rankable rows
- GPT-6 Sol
- 78.5
- #5/17
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V4.1 Flash
- 73.5
- Unranked · 1 rankable row
- GPT-6 Sol
- 82.8
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- GPT-6 Sol
- 80.2
- Estimated · #6/189
- Basis
- BenchAlign lane · 3 vs 5 public rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- GPT-6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- GPT-6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V4.1 Flash
- Not ranked
- GPT-6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V4.1 Flash | GPT-6 Sol | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 69.7Estimated · #7/157 | Not comparableBenchAlign lane · 9 vs 4 public rows | Not comparable |
| Coding | Not ranked | 74.3Estimated · #6/159 | Not comparableBenchAlign lane · 6 vs 1 public rows | Not comparable |
| Reasoning | 78.5Unranked · 2 rankable rows | 78.5#5/17 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 73.5Unranked · 1 rankable row | 82.8Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 80.2Estimated · #6/189 | Not comparableBenchAlign lane · 3 vs 5 public rows | Not comparable |
| Multilingual | 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 |
| Math | 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.
AutomationBench
Agentic
DeepSeek V4.1 Flash: 54.8%GPT-6 Sol: 33.2%Normalized gap 21.6DeepSeek V4.1 Flash source GPT-6 Sol 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 V4.1 Flash
- $0.0009
- Fits in one request
- GPT-6 Sol
- $0.007
- Fits in one request
DeepSeek V4.1 Flash 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
- GPT-6 Sol
- $0.13
- Fits in one request
DeepSeek V4.1 Flash 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
- GPT-6 Sol
- $0.18
- Fits in one request
DeepSeek V4.1 Flash has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek V4.1 FlashGPT-6 Sol
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V4.1 Flash
GPT-6 Sol
API model ID
DeepSeek V4.1 Flash
deepseek-flash
DeepSeek models and pricingGPT-6 Sol
gpt-6-sol
OpenAI GPT-6 Sol model documentationCached-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 & PricingGPT-6 Sol
$0.2 per 1M cached input tokens
OpenAI GPT-6 Sol model documentationDocumented inputs
DeepSeek V4.1 Flash
text, image
DeepSeek vision guideGPT-6 Sol
text, image
OpenAI GPT-6 Sol model documentationDocumented outputs
DeepSeek V4.1 Flash
GPT-6 Sol
Provider availability
DeepSeek V4.1 Flash
Generally Available · DeepSeek API, open weights
DeepSeek-V4.1-Flash releaseGPT-6 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex
OpenAI GPT-6 Sol and Luna launchReasoning profile
DeepSeek V4.1 Flash
Reasoning
GPT-6 Sol
Reasoning
Weight access
DeepSeek V4.1 Flash
Open Weight
GPT-6 Sol
Proprietary
License
DeepSeek V4.1 Flash
Open Weight
GPT-6 Sol
Proprietary
Release date
DeepSeek V4.1 Flash
2026-09-10
GPT-6 Sol
2026-09-16
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 $0.13. Cache-heavy agent loop: $0.0192 vs $0.18.
- Context tradeoff
- GPT-6 Sol has the larger documented window (1.05M).
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 evidence28 rows
Agentic
Terminal-Bench 2.1
DeepSeek V4.1 Flash90.6%SourceGPT-6 Sol—Not directly comparable
terminalBench3
DeepSeek V4.1 Flash30%SourceGPT-6 Sol—Not directly comparable
Terminal-Bench 4.0
DeepSeek V4.1 Flash31.20%SourceGPT-6 Sol—Not directly comparable
CyberGym
DeepSeek V4.1 Flash88.1%SourceGPT-6 Sol—Not directly comparable
ExploitGym
DeepSeek V4.1 Flash15.3%SourceGPT-6 Sol22.1%SourceGPT-6 Sol leads this result
HLE w/ tools
DeepSeek V4.1 Flash63.9%SourceGPT-6 Sol—Not directly comparable
AutomationBench
DeepSeek V4.1 Flash54.8%SourceGPT-6 Sol33.2%SourceDeepSeek V4.1 Flash leads this result
Agents' Last Exam
DeepSeek V4.1 Flash31.8%SourceGPT-6 Sol56.4%SourceGPT-6 Sol leads this result
Terminal-Bench 2.1 (Vals)
DeepSeek V4.1 Flash74.5%SourceGPT-6 Sol—Not directly comparable
OSWorld 2.0
DeepSeek V4.1 Flash—GPT-6 Sol60.5%SourceNot directly comparable
Coding
Codeforces
DeepSeek V4.1 Flash3471.0SourceGPT-6 Sol—Not directly comparable
Terminal-Bench 2.1
DeepSeek V4.1 Flash90.6%SourceGPT-6 Sol—Not directly comparable
terminalBench3
DeepSeek V4.1 Flash30%SourceGPT-6 Sol—Not directly comparable
DeepSWE
DeepSeek V4.1 Flash74.2%SourceGPT-6 Sol68.8%SourceDeepSeek V4.1 Flash leads this result
ProgramBench
DeepSeek V4.1 Flash20.3%SourceGPT-6 Sol—Not directly comparable
NL2Repo
DeepSeek V4.1 Flash65.4%SourceGPT-6 Sol—Not directly comparable
Multimodal
Knowledge
GPQA
DeepSeek V4.1 Flash90.9%SourceGPT-6 Sol—Not directly comparable
GPQA-D
DeepSeek V4.1 Flash90.9%SourceGPT-6 Sol—Not directly comparable
HLE
DeepSeek V4.1 Flash36.8%SourceGPT-6 Sol—Not directly comparable
HealthBench (raw)
DeepSeek V4.1 Flash—GPT-6 Sol47.1%SourceNot directly comparable
HealthBench (length-adjusted)
DeepSeek V4.1 Flash—GPT-6 Sol53.2%SourceNot directly comparable
HealthBench Professional
DeepSeek V4.1 Flash—GPT-6 Sol60.8%SourceNot directly comparable
HealthBench Professional (raw)
DeepSeek V4.1 Flash—GPT-6 Sol59.5%SourceNot directly comparable
HealthBench Hard
DeepSeek V4.1 Flash—GPT-6 Sol30.1%SourceNot directly comparable
Math
Apex
DeepSeek V4.1 Flash65.6%SourceGPT-6 Sol—Not directly comparable
Questions
Which is better, DeepSeek V4.1 Flash or GPT-6 Sol?
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 GPT-6 Sol?
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 GPT-6 Sol?
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 GPT-6 Sol?
For the stated presets, chat costs $0.0009 on DeepSeek V4.1 Flash and $0.007 on GPT-6 Sol; repository review costs $0.0186 and $0.13; the cache-heavy agent loop costs $0.0192 and $0.18. Costs use the listed standard API rates.
Which has the larger context window, DeepSeek V4.1 Flash or GPT-6 Sol?
GPT-6 Sol has the larger documented context window: 1.05M, compared with 1M.
Related comparisons
Last updated September 22, 2026
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