BenchLeader

DeepSeek V4 Flash

Reasoning effort

DeepSeek V4 Flash is a DeepSeek open-weights reasoning model, released 31 Jul 2026. Its best configuration (max reasoning effort) ranks #131 of 785 on the BenchLeader Index at 59.4 ±3.6, in the upper half. The ± is the point: 220 other configurations score within that range, so they and this one cannot be told apart on quality alone — price and speed are what separate them. It scores highest in agents & tools (68) and lowest in knowledge (49). At $0.175 per million tokens blended it is among the cheapest fifth of ranked models. Output speed of 150 tokens per second puts it in the fastest quarter, with a first answer in 14.7 s. It has been measured at 5 reasoning-effort settings; this summary describes the best-scoring one, and the tabs above switch between them. Last measured 31 Jul 2026.

Blended price
$0.175/M
$0.140 in · $0.280 out
Output speed
150 tok/s
measured by Artificial Analysis
First answer
15 s
first token 1.30 s
Context
1.0M
Full answer
18 s
median, reasoning included
Cost per run
$0.149
one full Intelligence Index run
Released
31 Jul 2026
How it scores by categoryDashed line = average model (50). One step of 15 = one standard deviation.
  1. Overall index59
  2. Reasoning64
  3. Coding56
  4. Agents & tools68
  5. Maths56
  6. Knowledge49
  7. Long context65
  8. Composite68

Versions

DeepSeek has shipped 2 models under this name. Each is ranked on its own results; a newer version often has fewer results so far, which holds its index nearer the average until more arrive.

ModelReleasedIndexRank
DeepSeek V4 Flashmaxthis page31 Jul 202659.4#131
DeepSeek V4 Flash 0420max24 Apr 202657.0#190

Reasoning-effort configurations

The same model behaves differently depending on how much it is allowed to think. Each row is one setting, scored only on the benchmarks that were run at that setting. “Not stated” collects results from publishers that did not say which setting they used; for a reasoning model that is usually its thinking mode, but we do not assume it. Pick a setting here or at the top of the page to see its price, speed and category scores.

EffortIndexRankSpeedFirst answerChat reply costCategories
no reasoning––51 tok/s1.30 s$0.0002Reasoning 31
low––51 tok/s1.30 s$0.0002Reasoning 56
high57.4#17951 tok/s1.30 s$0.0002Coding 56 · Human preference 62 · Knowledge 55 · Maths 62 · Reasoning 60
maxbest59.4#131150 tok/s15 s$0.0001Agents & tools 68 · Coding 56 · Composite 68 · Knowledge 49 · Long context 65 · Maths 56 · Reasoning 64
not stated52.2#32651 tok/s1.30 s$0.0002Agents & tools 54 · Coding 45 · Composite 39 · Human preference 62 · Maths 59 · Reasoning 58

How its index has moved

8 Sept 2026 to 23 Oct 2026
485766
  • max
  • high
  • not stated

The index is recomputed from scratch every day, so a line moves when a new benchmark result lands, when a publisher revises a score, or when the models it is normalised against change. Early movement usually means the score is still settling.

Benchmark results

See all LLM benchmarks. One column per reasoning effort. Rank is among every configuration of every model on that benchmark. Hover a score for the run it came from.

Reasoning

Benchmarkno reasoninglowhighmaxnot statedSourceTrend
GPQA Diamond–––91.0%#33–Epoch AI Benchmarking Hub
SimpleBencheffort not stated––––61.1%#26SimpleBench
Arena Hard Prompts––1458#101–1459#97Arena (LMArena)
LiveBench 2.0 Reasoningnot in indexeffort not stated––––70.6%#49LiveBench
LiveBench 1.0 Reasoningnot in indexeffort not stated––––86.6%#32LiveBench
GPQA Diamond (AA)not in index–––90.8%#55–Artificial Analysis
Humanity's Last Exam (AA)not in index–––38.5%#97–Artificial Analysis
GPQA Diamond (Vals)not in index––89.9%#27––Vals AI
Kagi LLM Benchmarkeffort not stated––––52.2%#76Kagi LLM Benchmark
ARC-AGI-111.8%#23384.0%#11487.0%#9989.0%#85–ARC Prize
ARC-AGI-22.1%#20146.0%#11256.0%#9961.4%#80–ARC Prize
CritPt–––16.6%#85–Artificial Analysis
Chess Puzzlesnot in index–––32.0%#64–Epoch AI Benchmarking Hub
Mystery Game Puzzlesnot in index–––34.0%#25–Epoch AI Benchmarking Hub
LMCAnot in index–––41.7%#102–Epoch AI Benchmarking Hub
DTBenchnot in index–––90.9%#76–Epoch AI Benchmarking Hub

Coding

Benchmarkno reasoninghighmaxnot statedSourceTrend
SciCode–42.0%#15149.9%#111–SciCode
WeirdML–57.0%#5663.0%#43–WeirdML
FrontierCodeeffort not stated–––18.8%#39Cognition
Arena Coding–1480#107–1484#101Arena (LMArena)
Arena WebDev–1582#35––Arena (LMArena)
LiveBench 2.0 Codingnot in indexeffort not stated–––77.0%#35LiveBench
LiveBench 1.0 Codingnot in indexeffort not stated–––75.0%#52LiveBench
SciCode (AA)not in index––50.4%#107–Artificial Analysis
LiveCodeBench–87.3%#16––Vals AI
IOI–32.7%#39––Vals AI
SWE-bench (Vals)not in index–88.8%#11––Vals AI
Code Migrationnot in index–38.6%#32––Vals AI
ProgramBenchnot in index–0.0%#32––Vals AI
Vibe Code Bench v1.1not in index–74.7%#36––Vals AI
ALE-Benchnot in index325.0#116678.2#871306.1#33–Epoch AI Benchmarking Hub
Terminal-Bench 4.0 (AA)not in index––12.1%#84–Artificial Analysis
Terminal-Bench 2.1 (AA)not in index––78.7%#49–Artificial Analysis

Agents & tools

Benchmarkhighmaxnot statedSourceTrend
LiveBench Long Agenticnot in indexeffort not stated––36.6%#29LiveBench
LiveBench Browsernot in indexeffort not stated––0.0%#48LiveBench
LiveBench Agentic Codingnot in indexeffort not stated––46.8%#50LiveBench
Terminal-Bench 2.1 (Vals)effort not stated––67.0%#36Vals AI
HiL-Bencheffort not stated––47.8%#13Scale AI SEAL
τ³-Banking (AA)–39.4%#33–Artificial Analysis
CyberBenchnot in index72.1%#17––Vals AI
Finance Agent v2not in index49.5%#47––Vals AI
Harvey's Legal Agent Benchmark v1not in index8.3%#26––Vals AI
Legal Research Benchnot in index30.3%#48––Vals AI
SkillsBenchnot in index50.7%#21––Vals AI
Tax Agent Benchnot in index28.1%#32––Vals AI
Terminal-Bench 4.0 (Vals)not in index18.7%#31––Vals AI
Terminal-Bench Sciencenot in index4.3%#25––Vals AI
AutomationBenchnot in index–54.0%#64–Artificial Analysis
GDP.pdf (AA)not in index–11.0%#126–Artificial Analysis

Maths

Benchmarkhighmaxnot statedSourceTrend
FrontierMath Tiers 1–3–57.5%#44–Epoch AI Benchmarking Hub
FrontierMath Tier 4–24.4%#50–Epoch AI Benchmarking Hub
OTIS Mock AIME–94.4%#54–Epoch AI Benchmarking Hub
ProofBencheffort not stated––56.0%#24Vals AI
LiveBench Mathematicsnot in indexeffort not stated––86.8%#49LiveBench
AIME 2026possible contamination–95.8%#13–MathArena
HMMT February 2026possible contamination–93.9%#10–MathArena
MathArena Apexpossible contamination–27.1%#11–MathArena
Arena Maths1442#90–1424#115Arena (LMArena)

Knowledge

Benchmarkhighmaxnot statedSourceTrend
SimpleQA Verified–33.6%#58–Epoch AI Benchmarking Hub
LiveBench 2.0 Data Analysisnot in indexeffort not stated––68.0%#37LiveBench
LiveBench Researchnot in indexeffort not stated––79.2%#19LiveBench
LiveBench 1.0 Data Analysisnot in indexeffort not stated––79.3%#14LiveBench
AA-Omniscience–-14.3#217–Artificial Analysis
MMLU-Pro86.2%#45––Vals AI
LegalBench77.7%#104––Vals AI
CorpFin61.9%#52––Vals AI
TaxEval70.7%#84––Vals AI
BioMysteryBenchnot in index64.4%#21––Vals AI
Excel Modeling Benchmarknot in index57.0%#43––Vals AI
MedCodenot in index41.4%#52––Vals AI
MedScribenot in index80.4%#56––Vals AI
AA-Omniscience: accuracynot in index–40.4%#122–Artificial Analysis
AA-Omniscience: non-hallucinationnot in index–8.3%#464–Artificial Analysis

Instruction following

Benchmarkhighnot statedSourceTrend
LiveBench Languagenot in indexeffort not stated–79.2%#37LiveBench
LiveBench Instruction Followingnot in indexeffort not stated–65.5%#43LiveBench
Arena Instruction Followingnot in index1434#911428#96Arena (LMArena)

Human preference

Benchmarkhighnot statedSourceTrend
Arena Text1439#951436#102Arena (LMArena)
Arena Creative Writingnot in index1410#881408#94Arena (LMArena)
Arena Multi-turnnot in index1446#941453#82Arena (LMArena)
Arena Longer Queriesnot in index1448#901447#91Arena (LMArena)

Multimodal

Benchmarknot statedSourceTrend
LiveBench Designnot in index71.7%#37LiveBench

Long context

BenchmarkmaxSourceTrend
AA-LCR79.7%#88Artificial Analysis

Composite

Benchmarkmaxnot statedSourceTrend
Epoch Capabilities Indexnot in indexeffort not stated–154.8#39Epoch AI Benchmarking Hub
LiveBench 2.0effort not stated–57.6%#40LiveBench
LiveBench 1.0not in indexeffort not stated–74.2%#42LiveBench
AA Intelligence Index v4.3.234.3#95–Artificial Analysis

Where to run it

Every provider serving this model through OpenRouter, with throughput and first-token latency measured on live traffic over the last 30 minutes and each provider’s own price. Purple marks the best in each column.

ProviderSpeedFirst tokenInput $/MOutput $/MContextQuantisation
Wafer95 tok/s0.71 s$0.700$0.1701.0Mmxfp4
Relace83 tok/s0.42 s$0.041$0.4501.0Mfp4
Alibaba Cloud Int.80 tok/s1.74 s$0.150$0.3001Mfp8
Cloudflare79 tok/s0.93 s$0.440$1.32384k–
Parasail78 tok/s0.69 s$0.140$0.2801.0Mfp8
Baidu Qianfan74 tok/s0.76 s$0.043$0.0871.0Mfp8
Azure66 tok/s1.45 s$0.210$0.5601.0M–
GMICloud49 tok/s1.71 s$0.091$0.1821.0Mfp8
DigitalOcean42 tok/s0.79 s$0.098$0.1961.0M–
StreamLake41 tok/s1.48 s$0.043$0.0861.0Mfp8
Venice34 tok/s1.35 s$0.097$0.1931M–
AtlasCloud32 tok/s1.70 s$0.140$0.2801.0Mfp4
SiliconFlow30 tok/s0.98 s$0.130$0.2801.0Mfp8
Mancer21 tok/s3.28 s$0.190$0.5001.0Mfp8
DeepInfra21 tok/s2.54 s$0.090$0.1801.0Mfp8

Price history

The price above, per 1M tokens, every day it changed.

$0.00$0.165$0.330$0.495$0.660Sept 26Oct 26
input outputnow $0.140 in · $0.280 out

What a task costs

Estimates from list price, output speed and time to first answer for the best configuration. “With caching” assumes three-quarters of the input is served from the prompt cache at $0.003 per 1M. Reasoning tokens are not modelled.

WorkloadTokens in / outCostWith cachingTime
Chat reply400 / 300$0.0001$0.000116.7 s
Summarize a 30-page report12,000 / 600$0.0018$0.000618.7 s
Code edit6,000 / 1,500$0.0013$0.000624.7 s
Agentic coding session60,000 / 4,000$0.0095$0.003441.4 s
Structured extraction2,000 / 200$0.0003$0.000116.0 s

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Questions people ask

What are DeepSeek V4 Flash's benchmark scores?
As of 24 Oct 2026, DeepSeek V4 Flash (max effort) scores 59.4 ±3.6 on the BenchLeader Index, strongest in agents & tools (68) and weakest in knowledge (49). That rests on 17 independent benchmark results, and every score on this page links to its publisher.
How does DeepSeek V4 Flash rank?
DeepSeek V4 Flash (max effort) is #131 of 785 ranked configurations on 24 Oct 2026. 220 other configurations score within its margin, so the exact position matters less than the band. The leader is Claude Opus 5.5 (max effort) at 72.6.
How much does DeepSeek V4 Flash cost per 1M tokens?
As of 24 Oct 2026, DeepSeek V4 Flash costs $0.140 per 1M input tokens and $0.280 per 1M output tokens, $0.175 blended (three input tokens per output token). The list price last changed on 18 Oct 2026.
Is DeepSeek V4 Flash better than GPT-5.6 Sol?
As of 24 Oct 2026, GPT-5.6 Sol (max) leads on quality (68.0 vs 59.4 on the BenchLeader Index), but DeepSeek V4 Flash (max) costs 46× less, so it is the better buy unless you need the extra quality. The head-to-head page has the benchmark-by-benchmark detail.
When was DeepSeek V4 Flash released?
DeepSeek released DeepSeek V4 Flash on 31 Jul 2026. BenchLeader has results for it at 5 reasoning-effort settings.

Data as of 24 Oct 2026. Compare these configurations.

Cite as: BenchLeader, “DeepSeek V4 Flash: benchmarks, pricing, speed and rank”, data as of 24 Oct 2026, https://www.benchleader.com/models/deepseek-v4-flash.