Enter opens the highlighted result. Esc closes.

GLM 4.7 Flash vs GPT-5.1-Codex-Mini

Updated October 21, 2026. 14 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.7 Flash Replace GPT-5.1-Codex-Mini
GLM 4.7 FlashZ.ai
Coding
No data
Overall
14.9
Output $/M
$0.40
8
GPT-5.1-Codex-MiniOpenAI
Coding
No data
Overall
20.4
Output $/M
$2.00
6
vs boards won of 14 shared

Decision reading

Neither model has a coding score yet, so the two cannot be ranked on coding.

GLM 4.7 Flash has the edge where output tokens cost 5.0x less, and where what matters is Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.

Model AGLM 4.7 FlashZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGPT-5.1-Codex-MiniOpenAINot benchmarked1 independent benchmarkNo cost per solved task
Shared results
14
GLM 4.7 Flash only
29
GPT-5.1-Codex-Mini only
8
Like-for-like categories
6 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
GLM 4.7 Flash not benchmarked, GPT-5.1-Codex-Mini not benchmarked.
Widest category
Agentic work, 36.0 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.7 Flash 98.8% against GPT-5.1-Codex-Mini 62.9%, 36.0 points apart on a 0 to 100 scale.
Output price
GLM 4.7 Flash $0.40 against GPT-5.1-Codex-Mini $2.00 per million output tokens, 5.0x apart.
Context window
GLM 4.7 Flash takes 200K tokens, GPT-5.1-Codex-Mini 400K.

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
CategoryGLM 4.7 FlashBarsGPT-5.1-Codex-MiniTies
Coding01
Intelligence01
Agentic work10
Reasoning02
Web design70
General02
All shared boards86
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
Coding benchmarks for GLM 4.7 Flash and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7 FlashGPT-5.1-Codex-MiniLeader
LMArena WebDevNot measured1244Only GPT-5.1-Codex-Mini measured
LiveCodeBench (Artificial Analysis run)Not measured83.6%Only GPT-5.1-Codex-Mini measured
Terminal-Bench Hard (Artificial Analysis run)22.0%33.3%GPT-5.1-Codex-Mini
Terminal-BenchNot measured61.6%Only GPT-5.1-Codex-Mini measured
LMArena Text: coding1382Not measuredOnly GLM 4.7 Flash measured
LMArena Text: software and IT1389Not measuredOnly GLM 4.7 Flash measured
LMArena WebDev: HTMLNot measured1253Only GPT-5.1-Codex-Mini measured
Neither model has a result on the other 31 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.7 Flash and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7 FlashGPT-5.1-Codex-MiniLeader
AA Intelligence Index14.920.4GPT-5.1-Codex-Mini
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 Flash and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7 FlashGPT-5.1-Codex-MiniLeader
τ²-Bench Telecom98.8%62.9%GLM 4.7 Flash
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
Reasoning benchmarks for GLM 4.7 Flash and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7 FlashGPT-5.1-Codex-MiniLeader
GPQA Diamond (Artificial Analysis run)58.1%81.3%GPT-5.1-Codex-Mini
Humanity's Last Exam (Artificial Analysis run)7.6%18.5%GPT-5.1-Codex-Mini
GPQA Diamond (Epoch AI run)60.5%Not measuredOnly GLM 4.7 Flash measured
LMArena Text: hard prompts1356Not measuredOnly GLM 4.7 Flash measured
LMArena Text: expert1357Not measuredOnly GLM 4.7 Flash measured
LMArena Text: hard prompts in English1369Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 15 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Math benchmarks for GLM 4.7 Flash and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7 FlashGPT-5.1-Codex-MiniLeader
AA Math IndexNot measured91.7Only GPT-5.1-Codex-Mini measured
AIME 2025 (Artificial Analysis run)Not measured91.7%Only GPT-5.1-Codex-Mini measured
OTIS Mock AIME58.3%Not measuredOnly GLM 4.7 Flash measured
LMArena Text: math1355Not measuredOnly GLM 4.7 Flash measured
LMArena Text: mathematical work1369Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 8 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Knowledge benchmarks for GLM 4.7 Flash and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7 FlashGPT-5.1-Codex-MiniLeader
MMLU Pro (Artificial Analysis run)Not measured82.0%Only GPT-5.1-Codex-Mini measured
LMArena Text: science1358Not measuredOnly GLM 4.7 Flash measured
LMArena Text: medicine and healthcare1343Not measuredOnly GLM 4.7 Flash measured
LMArena Text: legal and government1357Not measuredOnly GLM 4.7 Flash measured
LMArena Text: business and finance1343Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 12 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index70
Web design benchmarks for GLM 4.7 Flash and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7 FlashGPT-5.1-Codex-MiniLeader
Design Arena: websites11941111GLM 4.7 Flash
Design Arena: UI components12081081GLM 4.7 Flash
Design Arena: game dev11351092GLM 4.7 Flash
Design Arena: data viz11231096GLM 4.7 Flash
Design Arena: 3D1121988GLM 4.7 Flash
Design Arena: all code categories11781094GLM 4.7 Flash
Design Arena: SVG1038978GLM 4.7 Flash
Design Arena: ASCII artNot measured1104Only GPT-5.1-Codex-Mini measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
General benchmarks for GLM 4.7 Flash and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7 FlashGPT-5.1-Codex-MiniLeader
IFBench60.8%67.9%GPT-5.1-Codex-Mini
Long context reasoning (AA-LCR)41.7%66.7%GPT-5.1-Codex-Mini
LMArena Text1351Not measuredOnly GLM 4.7 Flash measured
LMArena Text: instruction following1327Not measuredOnly GLM 4.7 Flash measured
LMArena Text: creative writing1296Not measuredOnly GLM 4.7 Flash measured
LMArena Text: English1373Not measuredOnly GLM 4.7 Flash measured
LMArena Text: non-English1330Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Chinese1403Not measuredOnly GLM 4.7 Flash measured
LMArena Text: German1336Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Korean1283Not measuredOnly GLM 4.7 Flash measured
LMArena Text: French1332Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Spanish1350Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Russian1333Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Polish1291Not measuredOnly GLM 4.7 Flash measured
LMArena Text: multi-turn1342Not measuredOnly GLM 4.7 Flash measured
LMArena Text: longer queries1345Not measuredOnly GLM 4.7 Flash measured
LMArena Text: writing and language1320Not measuredOnly GLM 4.7 Flash measured
LMArena Text: entertainment and media1304Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 28 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 Flash and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7 FlashGPT-5.1-Codex-MiniLeader
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

GLM 4.7 Flash$0.0003Fits in one request
GPT-5.1-Codex-Mini$0.0013Fits in one request

GLM 4.7 Flash costs less

Repository review

50K input + 3K output tokens

GLM 4.7 Flash$0.0045Fits in one request
GPT-5.1-Codex-Mini$0.018Fits in one request

GLM 4.7 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 4.7 Flash$0.017Exceeds the context window
GPT-5.1-Codex-Mini$0.070Fits in one request

GLM 4.7 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 GLM 4.7 Flash and GPT-5.1-Codex-Mini
SpecificationZ.ai GLM 4.7 FlashOpenAI GPT-5.1-Codex-Mini
MakerZ.aiOpenAI
WeightsOpen weightsNo public weights listed
ReleasedJanuary 19, 2026November 13, 2025
Context window200K400K
Input $/Mtok$0.0653$0.25
Output $/Mtok$0.40$2.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 Flash better than GPT-5.1-Codex-Mini for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 4.7 Flash has the edge where output tokens cost 5.0x less, and where what matters is Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.

Which is cheaper, GLM 4.7 Flash or GPT-5.1-Codex-Mini?

GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; GPT-5.1-Codex-Mini costs $0.25 and $2.00. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.7 Flash or GPT-5.1-Codex-Mini?

GPT-5.1-Codex-Mini, with 400K tokens against 200K.

How many benchmarks is the GLM 4.7 Flash vs GPT-5.1-Codex-Mini comparison based on?

14 benchmarks have a result for both models as of October 21, 2026. GLM 4.7 Flash's coding index rests on 0 independent benchmarks and GPT-5.1-Codex-Mini's on 1. Where only one model has a result, the page says so instead of naming a winner.

Compare next