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GPT-5.2-Codex vs Step 3.5 Flash

Updated October 21, 2026. 7 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GPT-5.2-Codex Replace Step 3.5 Flash
GPT-5.2-CodexOpenAI
Coding
45.4
Overall
28.5
Output $/M
$14.00
6
Step 3.5 FlashStepFun
Coding
No data
Overall
17.0
Output $/M
$0.30
1
vs boards won of 7 shared

Decision reading

Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Step 3.5 Flash has none, so the two cannot be ranked on coding.

Step 3.5 Flash has the edge where output tokens cost 46.7x less, and where the benchmark that matters is τ²-Bench Telecom, where it leads.

Model AGPT-5.2-CodexOpenAI45.4/1003 independent benchmarks$0.43 per solved taskModel BStep 3.5 FlashStepFunNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
7
GPT-5.2-Codex only
15
Step 3.5 Flash only
28
Like-for-like categories
5 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GPT-5.2-Codex 45.4 from 3 benchmarks, Step 3.5 Flash not benchmarked.
Widest category
General, 15.2 points apart on average over 2 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): GPT-5.2-Codex 82.3% against Step 3.5 Flash 63.0%, 19.3 points apart on a 0 to 100 scale.
Output price
GPT-5.2-Codex $14.00 against Step 3.5 Flash $0.30 per million output tokens, 46.7x apart.
Context window
GPT-5.2-Codex takes 400K tokens, Step 3.5 Flash 262K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGPT-5.2-CodexBarsStep 3.5 FlashTies
Coding10
Intelligence10
Agentic work01
Reasoning20
General20
All shared boards61
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
Coding benchmarks for GPT-5.2-Codex and Step 3.5 Flash
BenchmarkGPT-5.2-CodexStep 3.5 FlashLeader
SWE-bench Verified72.8%Not measuredOnly GPT-5.2-Codex measured
SWE-bench Multilingual66.3%Not measuredOnly GPT-5.2-Codex measured
LMArena WebDev1339Not measuredOnly GPT-5.2-Codex measured
LiveCodeBench (Vals AI)88.0%Not measuredOnly GPT-5.2-Codex measured
SWE-bench Verified (Vals AI)72.4%Not measuredOnly GPT-5.2-Codex measured
Vibe Code Bench (Vals AI)37.9%Not measuredOnly GPT-5.2-Codex measured
Terminal-Bench Hard (Artificial Analysis run)37.1%32.6%GPT-5.2-Codex
Terminal-Bench66.5%Not measuredOnly GPT-5.2-Codex measured
ALE-Bench1299.9Not measuredOnly GPT-5.2-Codex measured
LMArena Text: codingNot measured1436Only Step 3.5 Flash measured
LMArena Text: software and ITNot measured1434Only Step 3.5 Flash measured
LMArena WebDev: React1329Not measuredOnly GPT-5.2-Codex measured
LMArena WebDev: HTML1348Not measuredOnly GPT-5.2-Codex measured
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GPT-5.2-Codex and Step 3.5 Flash
BenchmarkGPT-5.2-CodexStep 3.5 FlashLeader
AA Intelligence Index28.517.0GPT-5.2-Codex
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-5.2-Codex and Step 3.5 Flash
BenchmarkGPT-5.2-CodexStep 3.5 FlashLeader
Design Arena: agents building web apps1051Not measuredOnly GPT-5.2-Codex measured
Design Arena: agents building full-stack apps993Not measuredOnly GPT-5.2-Codex measured
Design Arena: agents building mobile apps1089Not measuredOnly GPT-5.2-Codex measured
Design Arena: agents building Android apps1176Not measuredOnly GPT-5.2-Codex measured
Design Arena: agents building Godot games1142Not measuredOnly GPT-5.2-Codex measured
τ²-Bench Telecom92.1%94.4%Step 3.5 Flash
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
Reasoning benchmarks for GPT-5.2-Codex and Step 3.5 Flash
BenchmarkGPT-5.2-CodexStep 3.5 FlashLeader
GPQA Diamond (Artificial Analysis run)89.9%83.1%GPT-5.2-Codex
Humanity's Last Exam (Artificial Analysis run)35.7%24.5%GPT-5.2-Codex
LMArena Text: hard promptsNot measured1411Only Step 3.5 Flash measured
LMArena Text: expertNot measured1421Only Step 3.5 Flash measured
LMArena Text: hard prompts in EnglishNot measured1421Only Step 3.5 Flash measured
Neither model has a result on the other 16 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Math benchmarks for GPT-5.2-Codex and Step 3.5 Flash
BenchmarkGPT-5.2-CodexStep 3.5 FlashLeader
LMArena Text: mathNot measured1408Only Step 3.5 Flash measured
LMArena Text: mathematical workNot measured1421Only Step 3.5 Flash measured
Neither model has a result on the other 11 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Knowledge benchmarks for GPT-5.2-Codex and Step 3.5 Flash
BenchmarkGPT-5.2-CodexStep 3.5 FlashLeader
LMArena Text: scienceNot measured1415Only Step 3.5 Flash measured
LMArena Text: medicine and healthcareNot measured1408Only Step 3.5 Flash measured
LMArena Text: legal and governmentNot measured1408Only Step 3.5 Flash measured
LMArena Text: business and financeNot measured1394Only Step 3.5 Flash measured
Neither model has a result on the other 13 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for GPT-5.2-Codex and Step 3.5 Flash
BenchmarkGPT-5.2-CodexStep 3.5 FlashLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
General benchmarks for GPT-5.2-Codex and Step 3.5 Flash
BenchmarkGPT-5.2-CodexStep 3.5 FlashLeader
IFBench77.6%66.5%GPT-5.2-Codex
Long context reasoning (AA-LCR)82.3%63.0%GPT-5.2-Codex
LMArena TextNot measured1403Only Step 3.5 Flash measured
LMArena Text: instruction followingNot measured1385Only Step 3.5 Flash measured
LMArena Text: creative writingNot measured1357Only Step 3.5 Flash measured
LMArena Text: EnglishNot measured1417Only Step 3.5 Flash measured
LMArena Text: non-EnglishNot measured1385Only Step 3.5 Flash measured
LMArena Text: ChineseNot measured1446Only Step 3.5 Flash measured
LMArena Text: GermanNot measured1405Only Step 3.5 Flash measured
LMArena Text: JapaneseNot measured1353Only Step 3.5 Flash measured
LMArena Text: KoreanNot measured1351Only Step 3.5 Flash measured
LMArena Text: FrenchNot measured1421Only Step 3.5 Flash measured
LMArena Text: SpanishNot measured1419Only Step 3.5 Flash measured
LMArena Text: RussianNot measured1385Only Step 3.5 Flash measured
LMArena Text: PolishNot measured1389Only Step 3.5 Flash measured
LMArena Text: multi-turnNot measured1404Only Step 3.5 Flash measured
LMArena Text: longer queriesNot measured1402Only Step 3.5 Flash measured
LMArena Text: writing and languageNot measured1372Only Step 3.5 Flash measured
LMArena Text: entertainment and mediaNot measured1360Only Step 3.5 Flash measured
Neither model has a result on the other 27 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-5.2-Codex and Step 3.5 Flash
BenchmarkGPT-5.2-CodexStep 3.5 FlashLeader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GPT-5.2-Codex$0.0088Fits in one request
Step 3.5 Flash$0.0003Fits in one request

Step 3.5 Flash costs less

Repository review

50K input + 3K output tokens

GPT-5.2-Codex$0.130Fits in one request
Step 3.5 Flash$0.0059Fits in one request

Step 3.5 Flash costs less

Agent loop

200K input + 10K output tokens

GPT-5.2-Codex$0.490Fits in one request
Step 3.5 Flash$0.023Fits in one request

Step 3.5 Flash costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GPT-5.2-Codex and Step 3.5 Flash
SpecificationOpenAI GPT-5.2-CodexStepFun Step 3.5 Flash
MakerOpenAIStepFun
WeightsNo public weights listedOpen weights
ReleasedJanuary 14, 2026January 29, 2026
Context window400K262K
Input $/Mtok$1.75$0.10
Output $/Mtok$14.00$0.30
Coding index45.4Not benchmarked
Independent measurements behind it30
Cost per solved task$0.43Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5.2-Codex better than Step 3.5 Flash for coding?

Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Step 3.5 Flash has none, so the two cannot be ranked on coding. Step 3.5 Flash has the edge where output tokens cost 46.7x less, and where the benchmark that matters is τ²-Bench Telecom, where it leads.

Which is cheaper, GPT-5.2-Codex or Step 3.5 Flash?

GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Step 3.5 Flash costs $0.10 and $0.30. Cost per solved task is known for GPT-5.2-Codex only.

Which has the bigger context window, GPT-5.2-Codex or Step 3.5 Flash?

GPT-5.2-Codex, with 400K tokens against 262K.

How many benchmarks is the GPT-5.2-Codex vs Step 3.5 Flash comparison based on?

7 benchmarks have a result for both models as of October 21, 2026. GPT-5.2-Codex's coding index rests on 3 independent benchmarks and Step 3.5 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.

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