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GPT-5.3-Codex vs Qwen3.6 27B

GPT-5.3-Codex leads Qwen3.6 27B on coding, 60.0 to 36.8 on the Vibe Coding Index, and Qwen3.6 27B costs 5.2x less per output token ($2.70 vs $14.00 per million). Updated October 21, 2026; 10 benchmarks have a result for both.

Replace GPT-5.3-Codex Replace Qwen3.6 27B
GPT-5.3-CodexOpenAI
Coding
60.0
Overall
No data
Output $/M
$14.00
9
Qwen3.6 27BQwen
Coding
36.8
Overall
45.3
Output $/M
$2.70
1
vs boards won of 10 shared

GPT-5.3-Codex or Qwen3.6 27B: which to pick

Pick GPT-5.3-Codex for coding: it scores 60.0 on the Vibe Coding Index against 36.8 for Qwen3.6 27B.

Pick Qwen3.6 27B for output tokens 5.2x cheaper ($2.70 vs $14.00 per million) and work measured by τ²-Bench Telecom, where it leads.

Model AGPT-5.3-CodexOpenAI60.0/1003 independent benchmarks$0.59 per solved taskModel BQwen3.6 27BQwen36.8/1002 independent benchmarks$1.30 per solved task
Shared results
10
GPT-5.3-Codex only
24
Qwen3.6 27B only
14
Like-for-like categories
5 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

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-5.3-Codex 60.0 from 3 benchmarks, Qwen3.6 27B 36.8 from 2 benchmarks.
Widest category
Coding, 25.3 points apart on average over 3 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): GPT-5.3-Codex 61.8% against Qwen3.6 27B 11.9%, 49.8 points apart on a 0 to 100 scale.
Output price
GPT-5.3-Codex $14.00 against Qwen3.6 27B $2.70 per million output tokens, 5.2x apart.
Context window
GPT-5.3-Codex takes 400K tokens, Qwen3.6 27B 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-5.3-CodexBarsQwen3.6 27BTies
Coding30
Intelligence20
Agentic work01
Reasoning20
General20
All shared boards91
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for GPT-5.3-Codex and Qwen3.6 27B
BenchmarkGPT-5.3-CodexQwen3.6 27BLeader
LMArena WebDev1407Not measuredOnly GPT-5.3-Codex measured
AA Coding IndexNot measured53.7Only Qwen3.6 27B measured
LiveCodeBench (Vals AI)87.3%Not measuredOnly GPT-5.3-Codex measured
SWE-bench Verified (Vals AI)78.0%70.0%GPT-5.3-Codex
Vibe Code Bench (Vals AI)61.8%11.9%GPT-5.3-Codex
IOI (Vals AI)53.8%Not measuredOnly GPT-5.3-Codex measured
SciCodeNot measured42.8%Only Qwen3.6 27B measured
Terminal-Bench Hard (Artificial Analysis run)53.0%34.8%GPT-5.3-Codex
Terminal-Bench78.4%Not measuredOnly GPT-5.3-Codex measured
WeirdML79.3%Not measuredOnly GPT-5.3-Codex measured
ALE-Bench1655.2Not measuredOnly GPT-5.3-Codex measured
LMArena WebDev: React1395Not measuredOnly GPT-5.3-Codex measured
LMArena WebDev: HTML1422Not measuredOnly GPT-5.3-Codex measured
LMArena WebDev: from an image1443Not measuredOnly GPT-5.3-Codex measured
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GPT-5.3-Codex and Qwen3.6 27B
BenchmarkGPT-5.3-CodexQwen3.6 27BLeader
AA Intelligence Index32.521.4GPT-5.3-Codex
Epoch Capabilities Index157.1146.2GPT-5.3-Codex
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-5.3-Codex and Qwen3.6 27B
BenchmarkGPT-5.3-CodexQwen3.6 27BLeader
AA Agentic IndexNot measured18.5Only Qwen3.6 27B measured
Design Arena: agents building web apps1032Not measuredOnly GPT-5.3-Codex measured
Design Arena: agents building full-stack apps983Not measuredOnly GPT-5.3-Codex measured
Design Arena: agents building mobile apps1054Not measuredOnly GPT-5.3-Codex measured
Design Arena: agents building Android apps1084Not measuredOnly GPT-5.3-Codex measured
Design Arena: agents building Godot games1124Not measuredOnly GPT-5.3-Codex measured
τ²-Bench Telecom86.0%94.2%Qwen3.6 27B
METR time horizons74.5%Not measuredOnly GPT-5.3-Codex measured
Vending-Bench 2$5,940Not measuredOnly GPT-5.3-Codex measured
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
Reasoning benchmarks for GPT-5.3-Codex and Qwen3.6 27B
BenchmarkGPT-5.3-CodexQwen3.6 27BLeader
GPQA Diamond (Artificial Analysis run)91.5%84.2%GPT-5.3-Codex
Humanity's Last Exam (Artificial Analysis run)42.5%23.1%GPT-5.3-Codex
GPQA Diamond (Epoch AI run)Not measured85.9%Only Qwen3.6 27B measured
Mystery game puzzlesNot measured7.0%Only Qwen3.6 27B measured
Neither model has a result on the other 18 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Math benchmarks for GPT-5.3-Codex and Qwen3.6 27B
BenchmarkGPT-5.3-CodexQwen3.6 27BLeader
OTIS Mock AIMENot measured91.1%Only Qwen3.6 27B measured
FrontierMath, tiers 1 to 3Not measured35.1%Only Qwen3.6 27B 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-5.3-Codex and Qwen3.6 27B
BenchmarkGPT-5.3-CodexQwen3.6 27BLeader
CaseLaw (Vals AI)Not measured53.2%Only Qwen3.6 27B measured
CorpFin (Vals AI)Not measured62.3%Only Qwen3.6 27B measured
TaxEval (Vals AI)Not measured71.3%Only Qwen3.6 27B measured
MortgageTax (Vals AI)Not measured68.3%Only Qwen3.6 27B measured
Neither model has a result on the other 14 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GPT-5.3-Codex and Qwen3.6 27B
BenchmarkGPT-5.3-CodexQwen3.6 27BLeader
Design Arena: websites1163Not measuredOnly GPT-5.3-Codex measured
Design Arena: UI components1137Not measuredOnly GPT-5.3-Codex measured
Design Arena: game dev1161Not measuredOnly GPT-5.3-Codex measured
Design Arena: data viz1164Not measuredOnly GPT-5.3-Codex measured
Design Arena: 3D1008Not measuredOnly GPT-5.3-Codex measured
Design Arena: all code categories1145Not measuredOnly GPT-5.3-Codex measured
Design Arena: SVG1129Not measuredOnly GPT-5.3-Codex measured
Design Arena: ASCII art1152Not measuredOnly GPT-5.3-Codex measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
General benchmarks for GPT-5.3-Codex and Qwen3.6 27B
BenchmarkGPT-5.3-CodexQwen3.6 27BLeader
SAGE (Vals AI)Not measured45.6%Only Qwen3.6 27B measured
IFBench75.4%67.6%GPT-5.3-Codex
Long context reasoning (AA-LCR)83.3%77.3%GPT-5.3-Codex
DTBenchNot measured78.1%Only Qwen3.6 27B measured
LMCANot measured34.5%Only Qwen3.6 27B measured
Neither model has a result on the other 41 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-5.3-Codex and Qwen3.6 27B
BenchmarkGPT-5.3-CodexQwen3.6 27BLeader
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-5.3-Codex$0.0088Fits in one request
Qwen3.6 27B$0.0018Fits in one request

Qwen3.6 27B costs less

Repository review

50K input + 3K output tokens

GPT-5.3-Codex$0.130Fits in one request
Qwen3.6 27B$0.031Fits in one request

Qwen3.6 27B costs less

Agent loop

200K input + 10K output tokens

GPT-5.3-Codex$0.490Fits in one request
Qwen3.6 27B$0.117Fits in one request

Qwen3.6 27B 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-5.3-Codex and Qwen3.6 27B
SpecificationOpenAI GPT-5.3-CodexQwen3.6 27B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedFebruary 24, 2026April 27, 2026
Context window400K262K
Input $/Mtok$1.75$0.45
Output $/Mtok$14.00$2.70
Coding index60.036.8
Independent measurements behind it32
Cost per solved task$0.59$1.30
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5.3-Codex better than Qwen3.6 27B for coding?

Pick GPT-5.3-Codex for coding: it scores 60.0 on the Vibe Coding Index against 36.8 for Qwen3.6 27B. Pick Qwen3.6 27B for output tokens 5.2x cheaper ($2.70 vs $14.00 per million) and work measured by τ²-Bench Telecom, where it leads.

Which is cheaper, GPT-5.3-Codex or Qwen3.6 27B?

GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Per solved SWE-bench Verified task, GPT-5.3-Codex costs $0.59 and Qwen3.6 27B $1.30.

Which has the bigger context window, GPT-5.3-Codex or Qwen3.6 27B?

GPT-5.3-Codex, with 400K tokens against 262K.

How many benchmarks is the GPT-5.3-Codex vs Qwen3.6 27B comparison based on?

10 benchmarks have a result for both models as of October 21, 2026. GPT-5.3-Codex's coding index rests on 3 independent benchmarks and Qwen3.6 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.

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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/gpt-5-3-codex-vs-qwen3-6-27b">LLMBenchmark.ai, GPT-5.3-Codex vs Qwen3.6 27B</a></p>