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DeepSeek V4.1 Flash 1017 vs GPT-5.3-Codex

Full profiles: DeepSeek V4.1 Flash 1017 (DeepSeek) and GPT-5.3-Codex (OpenAI): every benchmark, price and rank.

Updated · Data from 7 sources

As of 25 Oct 2026, DeepSeek V4.1 Flash 1017 (max) and GPT-5.3-Codex (xhigh) are neck and neck on quality (64.0 vs 63.8 on the BenchLeader Index, inside the ±6.3 margin of error), and DeepSeek V4.1 Flash 1017 (max) costs 9.2× less, which makes it the better buy for most workloads. Pick DeepSeek V4.1 Flash 1017 for coding (70 vs 61); pick GPT-5.3-Codex for agents & tools (80 vs 64).

DeepSeek V4.1 Flash 1017 benchmarks max DeepSeek GPT-5.3-Codex benchmarks xhigh OpenAIOpen in the comparison tool

Which to pick for…

The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.

Use casePickWhyConfidence
CodingDeepSeek V4.1 Flash 101770 vs 61 category score; Vibe Code Bench v1.1 87.7% vs 61.8%High
Agents & tool useGPT-5.3-Codex80 vs 64 category scoreHigh
ReasoningDeepSeek V4.1 Flash 101771 vs 69 category score; CritPt 24.0% vs 16.9%Medium
KnowledgeGPT-5.3-Codex66 vs 57 category score; AA-Omniscience 10.9 vs 4.7High
Long documentsEither66 vs 67 category score; AA-LCR 82.7% vs 83.3%Either
Images and other mediaDeepSeek V4.1 Flash 101764 vs 62 category score; MMMU-Pro 80.6% vs 78.5%Medium
Lowest costDeepSeek V4.1 Flash 1017$0.525 vs $4.81 per 1M blended; agentic coding session $0.023 vs $0.161–
Fastest first answerDeepSeek V4.1 Flash 101711.4 s (DeepSeek V4.1 Flash 1017 (max)) vs 60.6 s (GPT-5.3-Codex (xhigh)) to the first answer token–
High volume on a budgetNeither flagshipA smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs–

Key differences

  • DeepSeek V4.1 Flash 1017 (max) and GPT-5.3-Codex (xhigh) are neck and neck on quality (64.0 vs 63.8, within the ±6.3 margin).
  • DeepSeek V4.1 Flash 1017 (max) is stronger in coding, composite, maths, multimodal, reasoning.
  • GPT-5.3-Codex (xhigh) is stronger in agents & tools, knowledge, long context, instruction following.
  • DeepSeek V4.1 Flash 1017 (max) is 9.2× cheaper ($0.525 vs $4.81 per 1M blended).
  • DeepSeek V4.1 Flash 1017 (max) streams 2.3× faster (195 vs 87 tokens per second).

At a glance

SpecDeepSeek V4.1 Flash 1017GPT-5.3-Codex
DeveloperDeepSeekOpenAI
Released17 Oct 20265 Feb 2026
Context window (tokens)1,048,576400,000
Compared atDeepSeek V4.1 Flash 1017 (max)GPT-5.3-Codex (xhigh)
Open weightsYesNo
Price in / out / cached input per 1M$0.300 / $1.20 / $0.0060$1.75 / $14.00 / $0.175

Category by category

Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.

CategoryDeepSeek V4.1 Flash 1017 (max)GPT-5.3-Codex (xhigh)Leader
Reasoning70.668.6DeepSeek V4.1 Flash 1017
Coding69.960.9DeepSeek V4.1 Flash 1017
Agents & tools63.580.1GPT-5.3-Codex
Maths61.8–not comparable
Knowledge57.166.4GPT-5.3-Codex
Instruction following–73.5not comparable
Multimodal63.761.5DeepSeek V4.1 Flash 1017
Long context66.466.8level
Composite80.065.8DeepSeek V4.1 Flash 1017

Where sources disagree

Across the 8 benchmarks both have, DeepSeek V4.1 Flash 1017 leads 5, GPT-5.3-Codex leads 3. Every overall index that rates both puts DeepSeek V4.1 Flash 1017 ahead.

Overall indexDeepSeek V4.1 Flash 1017 (max)GPT-5.3-Codex (xhigh)Leader
BenchLeader Index64.063.8neck and neck
AA Intelligence Index v4.3.245.132.5DeepSeek V4.1 Flash 1017

Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).

Price, speed and latency

List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page; suite cost is what Artificial Analysis paid to run its whole Intelligence Index.

MeasureDeepSeek V4.1 Flash 1017 (max)GPT-5.3-Codex (xhigh)
Blended price per 1M tokens$0.525$4.81
Input / output per 1M$0.300 / $1.20$1.75 / $14.00
Cached input per 1M$0.0060$0.175
Price per task: chat reply$0.0005$0.0049
Price per task: summarize a 30-page report$0.0043$0.029
Price per task: code edit$0.0036$0.032
Price per task: agentic coding session$0.023$0.161
Price per task: structured extraction$0.0008$0.0063
Cost to run a full benchmark suite$0.262–
Output speed195 tok/s87 tok/s
Time to first answer11.4 s60.6 s
Time to a complete answer13.9 s66.4 s

Each model is compared at its best-ranked reasoning-effort setting. DeepSeek V4.1 Flash 1017 (max) scores 64.0 and takes 11 s to its first answer token. GPT-5.3-Codex (xhigh) scores 63.8 and takes 61 s to its first answer token. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.

Every benchmark

71 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.

MetricPublisherDeepSeek V4.1 Flash 1017 (max)GPT-5.3-Codex (xhigh)
BenchLeader IndexBenchLeader64.063.8
Agents & tools scoreBenchLeader63.580.1
Coding scoreBenchLeader69.960.9
Composite scoreBenchLeader80.065.8
Knowledge scoreBenchLeader57.166.4
Long context scoreBenchLeader66.466.8
Maths scoreBenchLeader61.8–
Multimodal scoreBenchLeader63.761.5
Reasoning scoreBenchLeader70.668.6
Instruction following scoreBenchLeader–73.5
Blended price $/MBenchLeader$0.525$4.81
Output speedBenchLeader195 tok/s87 tok/s
Time to first answerBenchLeader11.4 s60.6 s
Context windowBenchLeader1,048,576 tokens400,000 tokens
Reasoning
GPQA DiamondEpoch AI Benchmarking Hub93.2%–
LiveBench 2.0 ReasoningLiveBench89.1%†–
GPQA Diamond (AA)Artificial Analysis–91.5%
Humanity's Last Exam (AA)Artificial Analysis43.3%42.5%
ARC-AGI-1ARC Prize93.5%–
ARC-AGI-2ARC Prize78.1%–
CritPtArtificial Analysis24.0%16.9%
Chess PuzzlesEpoch AI Benchmarking Hub46.0%–
Mystery Game PuzzlesEpoch AI Benchmarking Hub49.0%–
Coding
SWE-bench Verified (Epoch)Epoch AI Benchmarking Hub–74.8%†
WeirdMLWeirdML–77.9%
Arena WebDevArena (LMArena)16651407†
LiveBench 2.0 CodingLiveBench96.0%†–
SciCode (AA)Artificial Analysis55.9%–
LiveCodeBenchVals AI–87.3%
IOIVals AI–53.8%
SWE-bench (Vals)Vals AI–78.0%
Code MigrationVals AI44.2%–
Code Migration v1.1Vals AI67.5%–
Vibe Code Bench v1.1Vals AI87.7%61.8%
ALE-BenchEpoch AI Benchmarking Hub–1655.2
Terminal-Bench 4.0 (AA)Artificial Analysis33.8%–
FrontierCode 1.1 (AA)Artificial Analysis43.7%–
Agents & tools
Terminal-BenchTerminal-Bench–78.4%†
LiveBench Long AgenticLiveBench50.9%†–
Show all 71 benchmarks
MetricPublisherDeepSeek V4.1 Flash 1017 (max)GPT-5.3-Codex (xhigh)
Agents & tools
LiveBench BrowserLiveBench76.0%†–
Terminal-Bench HardArtificial Analysis–53.0%
τ²-Bench Telecom (AA)Artificial Analysis–86.0%
HiL-BenchScale AI SEAL–4.3%†
GDPval-AA v2.1Artificial Analysis62.3%–
Finance Agent v2Vals AI57.0%–
Finance Agent v2.1Vals AI62.9%–
Harvey's Legal Agent Benchmark v1.1Vals AI14.2%–
Harvey's Legal Agent Benchmark v1Vals AI9.2%–
Legal Research BenchVals AI45.2%–
Tax Agent BenchVals AI44.4%–
Terminal-Bench 2.0 (Vals)Vals AI–64.0%†
Terminal-Bench 4.0 (Vals)Vals AI31.8%–
METR Time HorizonsMETR–74.5%†
Vending-Bench 2Andon Labs–5940.1†
AutomationBenchArtificial Analysis66.6%–
GDP.pdf (AA)Artificial Analysis17.2%–
AA-Briefcase v1.1Artificial Analysis1570–
Terminal-Bench-Science 0.1 (AA)Artificial Analysis12.9%–
CWE-Bench (AA)Artificial Analysis69.2%–
DeepSec-Bench (AA)Artificial Analysis24.9%–
CyberGym end-to-end (AA)Artificial Analysis76.3%–
Maths
FrontierMath Tiers 1–3Epoch AI Benchmarking Hub71.2%–
FrontierMath Tier 4Epoch AI Benchmarking Hub46.3%–
OTIS Mock AIMEEpoch AI Benchmarking Hub99.4%–
Knowledge
SimpleQA VerifiedEpoch AI Benchmarking Hub42.5%–
LiveBench 2.0 Data AnalysisLiveBench79.5%†–
LiveBench ResearchLiveBench79.5%†–
AA-OmniscienceArtificial Analysis4.710.9
Excel Modeling BenchmarkVals AI61.8%–
Excel Modeling Benchmark v1.1Vals AI69.2%–
AA-Omniscience: accuracyArtificial Analysis42.9%52.9%
AA-Omniscience: non-hallucinationArtificial Analysis33.3%10.8%
Instruction following
IFBenchArtificial Analysis–75.4%
Multimodal
LiveBench DesignLiveBench83.4%†–
MMMU-ProArtificial Analysis80.6%78.5%
Long context
AA-LCRArtificial Analysis82.7%83.3%
MLCRArtificial Analysis26.7%–
Composite
Epoch Capabilities IndexEpoch AI Benchmarking Hub–157.1†
LiveBench 2.0LiveBench79.2%†–
AA Intelligence Index v4.3.2Artificial Analysis45.132.5
Vals IndexVals AI61.3–
Vals Finance IndexVals AI60.9%–
Vals Router IndexVals AI61.3%†–
AA Cyber IndexArtificial Analysis56.8–
Metadata
AA Openness IndexArtificial Analysis44.4–

Data as of 2026-10-25. Best configuration of each model; every score links to its source on the model pages.

What changed

  • DeepSeek V4.1 Flash 1017 (max): 10 new results (MLCR, AA Cyber Index, CWE-Bench (AA)…).
  • DeepSeek V4.1 Flash 1017 (max): BenchLeader Index 63.1 → 63.9.
  • DeepSeek V4.1 Flash 1017 (max): 11 new results (GPQA Diamond, FrontierMath Tiers 1–3, FrontierMath Tier 4…).
  • DeepSeek V4.1 Flash 1017 (max): 1 new result (FrontierCode 1.1 (AA)); 2 score revisions.
  • GPT-5.3-Codex (xhigh): BenchLeader Index 64.4 → 63.9.
  • GPT-5.3-Codex (xhigh): BenchLeader Index 64.9 → 64.4.

Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs

DeepSeek V4.1 Flash 1017 vs GPT-5.3-Codex: questions

Is DeepSeek V4.1 Flash 1017 better than GPT-5.3-Codex?
DeepSeek V4.1 Flash 1017 (max) and GPT-5.3-Codex (xhigh) are neck and neck on quality (64.0 vs 63.8, within the ±6.3 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2026-10-25, so check the category scores for your use.
Which is cheaper, DeepSeek V4.1 Flash 1017 or GPT-5.3-Codex, and will that change?
DeepSeek V4.1 Flash 1017 is cheaper today: $0.525 against $4.81 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
Is DeepSeek V4.1 Flash 1017 better than GPT-5.3-Codex for coding?
DeepSeek V4.1 Flash 1017 scores higher in coding (70 vs 61 on the category index, where 50 is average).
Is DeepSeek V4.1 Flash 1017 better than GPT-5.3-Codex for agents and tool use?
GPT-5.3-Codex scores higher in agentic tasks (80 vs 64 on the category index, where 50 is average).
Which is better for long documents?
They are level on long-context benchmarks (66 vs 67). DeepSeek V4.1 Flash 1017 accepts more context: 1,048,576 against 400,000 tokens for GPT-5.3-Codex.
Which has lower latency?
DeepSeek V4.1 Flash 1017 (max) answers first: 11.4 s against 60.6 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
Which is faster, DeepSeek V4.1 Flash 1017 or GPT-5.3-Codex?
DeepSeek V4.1 Flash 1017 streams faster: 195 against 87 output tokens per second.
Do independent benchmarks agree on DeepSeek V4.1 Flash 1017 vs GPT-5.3-Codex?
Across the 8 benchmarks both have, DeepSeek V4.1 Flash 1017 leads 5, GPT-5.3-Codex leads 3. Every overall index that rates both puts DeepSeek V4.1 Flash 1017 ahead.
When was DeepSeek V4.1 Flash 1017 released?
DeepSeek V4.1 Flash 1017 was released on 17 Oct 2026.