Model comparison

DeepSeek V4 Flash vs GPT-5.5 Instant

DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 42.7 on the Noometry Index.

Last verified . 25 shared benchmarks.

DeepSeek V4 Flash DeepSeek

53.6

Rank #35 Confirmed

GPT-5.5 Instant OpenAI

42.7

Rank #110 Confirmed

Summary

  • They share 25 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 8 categories and GPT-5.5 Instant in 0 categories; 5 gaps are clear of the uncertainty.
  • The widest gap is in math, where DeepSeek V4 Flash leads 60.3 to 26.5.
  • The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 57.5% for DeepSeek V4 Flash and 26.3% for GPT-5.5 Instant.
  • DeepSeek V4 Flash has downloadable open weights; the other is API-only.

Side by side

DeepSeek V4 Flash and GPT-5.5 Instant specifications
DeepSeek V4 FlashGPT-5.5 Instant
ProviderDeepSeekOpenAI
Noometry Index53.642.7
Released2026-04-242026-05-05
WeightsOpenProprietary
Context window1M—
Max output393K—
Input $ / M tokens$0.15—
Output $ / M tokens$0.60—
Results tracked4127

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Category by category

Coding DeepSeek V4 Flash leads

DeepSeek V4 Flash: 47.9 (#59), GPT-5.5 Instant: 44.3 (#74)

Coding benchmarks
BenchmarkDeepSeek V4 FlashGPT-5.5 Instant
SciCode49.9%48.6%
LMArena Coding14571433
FrontierCode18.8%—
LMArena WebDev1582—
WeirdML63%—
ALE-Bench1,306—

Reasoning DeepSeek V4 Flash leads

DeepSeek V4 Flash: 53.7 (#30), GPT-5.5 Instant: 24.9 (#155)

Reasoning benchmarks
BenchmarkDeepSeek V4 FlashGPT-5.5 Instant
CritPt16.6%0%
Chess Puzzles33%12%
LMArena Hard Prompts14441426
Epoch Capabilities Index154.49142.52
ARC-AGI-261.4%—
SimpleBench61.1%—
Kagi LLM Benchmark52.2%—
NYT Connections (extended)89.6%—
ARC-AGI-189%—
Mystery Game Puzzles34%—
DTBench90.9%—
LMCA41.7%—

Math DeepSeek V4 Flash leads

DeepSeek V4 Flash: 60.3 (#37), GPT-5.5 Instant: 26.5 (#259)

Math benchmarks
BenchmarkDeepSeek V4 FlashGPT-5.5 Instant
FrontierMath (Tiers 1-3)57.5%26.3%
FrontierMath Tier 424.4%2.4%
OTIS Mock AIME 2024-202594.4%68.1%
LMArena Math14271420
MathArena Final-Answer Competitions76.5%—
ProofBench56%—

Knowledge DeepSeek V4 Flash leads

DeepSeek V4 Flash: 55.4 (#48), GPT-5.5 Instant: 48.9 (#74)

Knowledge benchmarks
BenchmarkDeepSeek V4 FlashGPT-5.5 Instant
GPQA Diamond91%82.5%
LMArena Expert14411409
SimpleQA Verified33.6%—

Multimodal Not comparable

DeepSeek V4 Flash: —, GPT-5.5 Instant: 40.0 (#52)

Multimodal benchmarks
BenchmarkDeepSeek V4 FlashGPT-5.5 Instant
LMArena Vision—1250
LMArena Document—1403

Multilingual Too close to call

DeepSeek V4 Flash: 53.0 (#72), GPT-5.5 Instant: 52.8 (#80)

Multilingual benchmarks
BenchmarkDeepSeek V4 FlashGPT-5.5 Instant
LMArena Non-English14201417
LMArena Chinese14681456
LMArena French14391428
LMArena German14181411
LMArena Japanese14061408
LMArena Korean13841392
LMArena Russian14281431
LMArena Spanish14361429

Instruction Following Too close to call

DeepSeek V4 Flash: 74.9 (#81), GPT-5.5 Instant: 74.2 (#100)

Instruction Following benchmarks
BenchmarkDeepSeek V4 FlashGPT-5.5 Instant
LMArena Instruction Following14211406

Long Context Too close to call

DeepSeek V4 Flash: 43.8 (#85), GPT-5.5 Instant: 43.4 (#96)

Long Context benchmarks
BenchmarkDeepSeek V4 FlashGPT-5.5 Instant
LMArena Longer Query14341422

Writing & Preference DeepSeek V4 Flash leads

DeepSeek V4 Flash: 63.8 (#61), GPT-5.5 Instant: 61.8 (#85)

Writing & Preference benchmarks
BenchmarkDeepSeek V4 FlashGPT-5.5 Instant
LMArena Text14321419
LMArena Creative Writing14031419
LMArena Multi-Turn14491433
EQ-Bench Creative Writing1559—

Frequently asked questions

Is DeepSeek V4 Flash better than GPT-5.5 Instant?

DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 42.7 on the Noometry Index.

Is DeepSeek V4 Flash or GPT-5.5 Instant better for coding?

DeepSeek V4 Flash scores higher on coding benchmarks: 47.9 versus 44.3 in the Noometry coding category.

How many benchmarks do DeepSeek V4 Flash and GPT-5.5 Instant share?

25 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and GPT-5.5 Instant has 27.

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