The official model card shows that DeepSeek-V4.1-Flash is a multimodal MoE with a 552B backbone and 8B (prefill) / 16B (decode) activated per token; with the specified maximum reasoning effort and Agent harness, it scores 90.6 on Terminal-Bench 2.1 and 74.2 on DeepSWE v1.1, both above DeepSeek-V4-Flash in the table. Those numbers demonstrate the combined performance of the “model + harness + parameters” configuration and cannot be generalized as uniform capability across all clients or ordinary conversational tasks.
Hugging Face (DeepSeek official model card) · Read evidenceDeepSeek V4.1 Flash · Reviews and evidence
Which DeepSeek V4.1 Flash conclusions hold up?
Browse public evaluations by topic, source identity, and evidence type. Different versions, tiers, and harnesses are not treated as directly comparable.
This is a third-party source navigator, not a Tabbit test. Use the original source for live metrics; unknown values remain unknown.
Editorial takeaways
Editorial takeaways
Arena.ai reports a **+4.87%** net improvement for DeepSeek-V4.1-Flash (Max) relative to the Arena baseline. The body of the post states a **$0.07** median cost per task, while the detailed table states **$0.06**. The cost should therefore be recorded as an inconsistency in the original post, rather than combined into one unconditionally certain figure.
X · Read evidenceAt the `Reasoning, Max Effort` setting, Artificial Analysis gives DeepSeek V4.1 Flash an Artificial Analysis Intelligence Index score of about 40. It is attractive for agent tasks, long-context work, and cost, but its outputs are extremely verbose, and its AA-Omniscience Non-Hallucination Rate is only 4%, so “cheap” cannot be equated directly with reliable.
X (Artificial Analysis) · Read evidenceFull reviews and related reading
Selected evidence
Media / benchmarkIndependent measurement
DeepSeek-V4.1-Flash Official Model Card Benchmarks: Agent Strengths and Harness Boundaries
The official model card shows that DeepSeek-V4.1-Flash is a multimodal MoE with a 552B backbone and 8B (prefill) / 16B (decode) activated per token; with the specified maximum reasoning effort and Agent harness, it scores 90.6 on Terminal-Bench 2.1 and 74.2 on DeepSWE v1.1, both above DeepSeek-V4-Flash in the table. Those numbers demonstrate the combined performance of the “model + harness + parameters” configuration and cannot be generalized as uniform capability across all clients or ordinary conversational tasks.
SourceHugging Face (DeepSeek official model card)
PublishedUnknown
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash
- Source
- https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash
- Collection/review
- 2026-09-20; the dynamic source was not reopened
CommunityIndependent measurement
DeepSeek-V4.1-Flash (Max): Task Cost and Net Improvement in Agent Arena
Arena.ai reports a **+4.87%** net improvement for DeepSeek-V4.1-Flash (Max) relative to the Arena baseline. The body of the post states a **$0.07** median cost per task, while the detailed table states **$0.06**. The cost should therefore be recorded as an inconsistency in the original post, rather than combined into one unconditionally certain figure.
SourceX
Published2026-09-15
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash
- Source
- https://x.com/arena/status/2099606881845321841
- Collection/review
- 2026-09-20; the dynamic source was not reopened
CommunityIndependent measurement
Artificial Analysis: DeepSeek V4.1 Flash's Intelligence, Cost, and Hallucination Boundaries
At the `Reasoning, Max Effort` setting, Artificial Analysis gives DeepSeek V4.1 Flash an Artificial Analysis Intelligence Index score of about 40. It is attractive for agent tasks, long-context work, and cost, but its outputs are extremely verbose, and its AA-Omniscience Non-Hallucination Rate is only 4%, so “cheap” cannot be equated directly with reliable.
SourceX (Artificial Analysis)
Published2026-09-11
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash
- Source
- https://x.com/ArtificialAnlys/status/2098148674203488422
- Collection/review
- 2026-09-20; the dynamic source was not reopened
CommunityPersonal experience
DeepSeek V4.1 Flash Generates a Browser Tactical Shooter in OpenCode in a Single Run: A Field Report on the Artifact and Cost
Tarun122 says they used a large prompt in OpenCode to have DeepSeek V4.1 Flash generate a playable browser tactical shooter in about ten minutes, at a total cost of approximately $0.28 and roughly 300k tokens; the public demo page and code repository support the claim that the artifact exists, but the original post provides no complete prompt, configuration, execution logs, or baseline. This is therefore a personal field report with a public result, rather than a controlled evaluation.
SourceReddit, r/opencode
Published2026-09-10
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash
- Source
- https://www.reddit.com/r/opencode/comments/1wcd5vf/deepseek_v41_flash_is_cheap_fast_and_really_smart/
- Collection/review
- 2026-09-20; the dynamic source was not reopened
All sources
All sources
Media / benchmarkIndependent measurement
DeepSeek-V4.1-Flash Official Model Card Benchmarks: Agent Strengths and Harness Boundaries
The official model card shows that DeepSeek-V4.1-Flash is a multimodal MoE with a 552B backbone and 8B (prefill) / 16B (decode) activated per token; with the specified maximum reasoning effort and Agent harness, it scores 90.6 on Terminal-Bench 2.1 and 74.2 on DeepSWE v1.1, both above DeepSeek-V4-Flash in the table. Those numbers demonstrate the combined performance of the “model + harness + parameters” configuration and cannot be generalized as uniform capability across all clients or ordinary conversational tasks.
SourceHugging Face (DeepSeek official model card)
PublishedUnknown
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash
- Source
- https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash
- Collection/review
- 2026-09-20; the dynamic source was not reopened
CommunityIndependent measurement
DeepSeek-V4.1-Flash (Max): Task Cost and Net Improvement in Agent Arena
Arena.ai reports a **+4.87%** net improvement for DeepSeek-V4.1-Flash (Max) relative to the Arena baseline. The body of the post states a **$0.07** median cost per task, while the detailed table states **$0.06**. The cost should therefore be recorded as an inconsistency in the original post, rather than combined into one unconditionally certain figure.
SourceX
Published2026-09-15
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash
- Source
- https://x.com/arena/status/2099606881845321841
- Collection/review
- 2026-09-20; the dynamic source was not reopened
CommunityIndependent measurement
Artificial Analysis: DeepSeek V4.1 Flash's Intelligence, Cost, and Hallucination Boundaries
At the `Reasoning, Max Effort` setting, Artificial Analysis gives DeepSeek V4.1 Flash an Artificial Analysis Intelligence Index score of about 40. It is attractive for agent tasks, long-context work, and cost, but its outputs are extremely verbose, and its AA-Omniscience Non-Hallucination Rate is only 4%, so “cheap” cannot be equated directly with reliable.
SourceX (Artificial Analysis)
Published2026-09-11
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash
- Source
- https://x.com/ArtificialAnlys/status/2098148674203488422
- Collection/review
- 2026-09-20; the dynamic source was not reopened
CommunityPersonal experience
DeepSeek V4.1 Flash Generates a Browser Tactical Shooter in OpenCode in a Single Run: A Field Report on the Artifact and Cost
Tarun122 says they used a large prompt in OpenCode to have DeepSeek V4.1 Flash generate a playable browser tactical shooter in about ten minutes, at a total cost of approximately $0.28 and roughly 300k tokens; the public demo page and code repository support the claim that the artifact exists, but the original post provides no complete prompt, configuration, execution logs, or baseline. This is therefore a personal field report with a public result, rather than a controlled evaluation.
SourceReddit, r/opencode
Published2026-09-10
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash
- Source
- https://www.reddit.com/r/opencode/comments/1wcd5vf/deepseek_v41_flash_is_cheap_fast_and_really_smart/
- Collection/review
- 2026-09-20; the dynamic source was not reopened
Media / benchmarkEditorial analysis
DeepSeek V4.1 Flash on the AI IQ Leaderboard: Composite Score and Benchmark Coverage
At the time of collection, the AI IQ page estimated DeepSeek V4.1 Flash at IQ 116, ranked #38/138. This IQ is a derived estimate across six equally weighted capability dimensions; missing benchmarks enter a conservative imputation process, so it should not be treated as a direct intelligence quotient or as a single complete independent test. The visible benchmark coverage is concentrated in Academic Reasoning (4/6) and Reliability (1/7), while the other four dimensions are 0/3, 0/5, 0/6, and 0/6.
SourceAI IQ
PublishedUnknown
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash; exact snapshot follows the source
- Source date
- not disclosed
- Method/client
- Source-specific benchmark, API, or client; see conditions
CommunityPersonal experience
DeepSeek V4.1 Flash Fixing Legacy Code in OpenCode: Community Experience and False-Positive Boundaries
danilofs says that, after using DeepSeek V4.1 Flash in OpenCode continuously since Saturday, it cleaned up the mess left by Muse Code, Claude Code, and Codex over the course of a month; comments caution that it may report design choices as bugs, so constraints and human review are still required. This is a community field report, not a controlled test.
SourceReddit, r/DeepSeek
Published2026-09-15
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash; exact snapshot follows the source
- Source date
- 2026-09-15
- Method/client
- Source-specific benchmark, API, or client; see conditions
CommunityPersonal experience
DeepSeek V4.1 Flash in Hermes: Reddit Firsthand Experience with Proactivity and Overexecution
This discussion describes DeepSeek V4.1 Flash as “more proactive” rather than proven “smarter”: in Hermes, it may perform more irrelevant exploration, increasing tool calls and context consumption. It is suitable as a risk signal for Agent behavior, not as a measure of intelligence or a controlled evaluation.
SourceReddit (r/hermesagent)
Published2026-09-13
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash; exact snapshot follows the source
- Source date
- 2026-09-13
- Method/client
- Source-specific benchmark, API, or client; see conditions
CommunityPersonal experience
DeepSeek V4.1 Flash Forgetting Rules While Building a Game Engine in DSH
The original poster says the model was very fast when paired with DSH to develop a game engine, but completed far less work than Opus and forgot Markdown rules it had already read; this is a personal risk signal for complex Agent tasks and cannot be attributed solely to the model or DSH.
SourceReddit, r/DeepSeek
PublishedUnknown
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash; exact snapshot follows the source
- Source date
- not disclosed
- Method/client
- Source-specific benchmark, API, or client; see conditions
CommunityPersonal experience
A Six-Minute Landing Page with DeepSeek V4.1 Flash in DSH PTC Mode: Cache Costs and a Two-Turn Caveat
nehuenpereyra says they used DeepSeek V4.1 Flash with maximum reasoning in DeepSeek Harness's PTC mode to complete a Nura Health landing page in about six minutes with a single task, correctly generating the mobile version as well; the total was 3,422,844 tokens, and the post reports a cost of about $0.072. This was a single field experience, while the comments explain that quota exhaustion made the interface show two turns, so it cannot be treated as a controlled experiment or a stable success rate.
SourceReddit, r/DeepSeek
Published2026-09-10
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash; exact snapshot follows the source
- Source date
- 2026-09-10
- Method/client
- Source-specific benchmark, API, or client; see conditions
CommunityPersonal experience
DeepSeek V4.1 Flash: Complex Coding Boundaries and Luna Task Duration Comparison
This discussion supports a narrow conclusion: marty4286 said V4.1 Flash had replaced Luna Max, cutting runs that had taken about 1 hour to 20–40 minutes; similar tasks with Sol high took about 15–30 minutes and cost less. But the task, inputs, client, parameters, and acceptance criteria were not disclosed, so this cannot be treated as a controlled comparison of speed or capability.
SourceReddit, r/DeepSeek
Published2026-09-10
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash; exact snapshot follows the source
- Source date
- 2026-09-10
- Method/client
- Source-specific benchmark, API, or client; see conditions
CommunityPersonal experience
DeepSeek V4.1 Flash's Limits in Large C++ Codebases and GPU Acceleration: Reddit Community Opinions
Organic_Rip2483 considers DeepSeek V4.1 Flash lightweight, fast, and low-cost, making it suitable for web development, general programming, and Agent tasks; but says it starts to struggle with moderately complex C++, GPU acceleration, and large codebases. This is a signal from personal experience and cannot establish the model's accuracy or stability in this area.
SourceReddit, r/DeepSeek
Published2026-09-15
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash; exact snapshot follows the source
- Source date
- 2026-09-15
- Method/client
- Source-specific benchmark, API, or client; see conditions
CommunityPersonal experience
DeepSeek V4.1 Flash Roleplay: Diverging Experiences with Preset Adaptation and Runaway Long Replies
This discussion offers no single answer: CptPhantasmic found V4.1 Flash promising for writing style, instruction following, and detail handling in a short Janitor test, while MikabellStarfall encountered single replies exceeding **2,000 words**, loss of character control, and forgetting. Both emphasize the need to adjust the prompt or character card.
SourceReddit, r/SillyTavernAI
Published2026-09-10
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash; exact snapshot follows the source
- Source date
- 2026-09-10
- Method/client
- Source-specific benchmark, API, or client; see conditions
CommunityPersonal experience
Reddit in the Field: Astra Orchestration and DeepSeek Subagent Costs
Artforartsake99 says that with Astra (extra high) handling orchestration and DeepSeek V4.1 Flash handling execution, the former consumed 10% of the weekly subscription allowance while the latter incurred $.94 in API charges; this is a personal record and cannot establish stable cost or success rates.
SourceReddit, r/vibecoding
Published2026-09-14
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash; exact snapshot follows the source
- Source date
- 2026-09-14
- Method/client
- Source-specific benchmark, API, or client; see conditions
CommunityPersonal experience
DeepSeek V4.1 Flash: User Reports on Service Unavailability and Workflow Continuity
Several users in this discussion reported that the service was unavailable: some found alternative models too slow, one had a Vibecoding session interrupted, and another said the outage occurred just after completing a two-hour task. The original post lacks complete timing information, logs, and official confirmation, so it can only serve as community feedback on service continuity and the cost of switching models; it cannot establish the model's intelligence, stability, or the cause of the outage.
SourceReddit, r/DeepSeek
PublishedUnknown
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash; exact snapshot follows the source
- Source date
- not disclosed
- Method/client
- Source-specific benchmark, API, or client; see conditions
CommunityPersonal experience
DeepSeek V4.1 Flash: The Debate Over Cost and Token Efficiency in Complex Debugging
Uriziel01 says V4.1 Flash used only about 8% of the five-hour allowance after nearly two hours of complex debugging; commenters argued that this was more likely a result of caching and the 4× promotion, rather than evidence of high Token-generation efficiency. The author also acknowledges that GPT Sol max is clearly stronger on the hardest tasks.
SourceReddit, r/opencodeCLI
PublishedUnknown
CollectedUnknown
Unverified: the original source could not be rechecked.
- Model/version
- DeepSeek V4.1 Flash; exact snapshot follows the source
- Source date
- not disclosed
- Method/client
- Source-specific benchmark, API, or client; see conditions
DeepSeek V4.1 Flash
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