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GLM 5 vs GPT Chat Latest

Updated October 21, 2026. 0 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

GLM 5Z.ai
Coding
41.7
Overall
27.9
Output $/M
$3.20
GPT Chat LatestOpenAI
Coding
No data
Overall
No data
Output $/M
$30.00
vs $3.20 vs $30.00 per 1M output tokens. GLM 5 is 9.4x cheaper.

Decision reading

Only GLM 5 has a coding score, 41.7 on the Vibe Coding Index; GPT Chat Latest has none, so the two cannot be ranked on coding.

GPT Chat Latest has the edge where the context window is larger, 400K against 205K tokens.

Shared results
0
GLM 5 only
75
GPT Chat Latest only
0
Like-for-like categories
0 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 5 41.7 from 3 benchmarks, GPT Chat Latest not benchmarked.
Widest category
No category has a result for both models.
Widest single gap
No benchmark has a result for both models.
Output price
GLM 5 $3.20 against GPT Chat Latest $30.00 per million output tokens, 9.4x apart.
Context window
GLM 5 takes 205K tokens, GPT Chat Latest 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.

CodingDirectional onlySix of these boards feed the Vibe Coding Index14 boards, none shared
Coding benchmarks for GLM 5 and GPT Chat Latest
BenchmarkGLM 5GPT Chat LatestLeader
SWE-bench Verified72.8%Not measuredOnly GLM 5 measured
SWE-bench Multilingual69.7%Not measuredOnly GLM 5 measured
LMArena WebDev1435Not measuredOnly GLM 5 measured
LiveCodeBench (Vals AI)81.9%Not measuredOnly GLM 5 measured
SWE-bench Verified (Vals AI)71.4%Not measuredOnly GLM 5 measured
Vibe Code Bench (Vals AI)23.4%Not measuredOnly GLM 5 measured
Terminal-Bench Hard (Artificial Analysis run)43.2%Not measuredOnly GLM 5 measured
Terminal-Bench52.4%Not measuredOnly GLM 5 measured
WeirdML48.2%Not measuredOnly GLM 5 measured
ALE-Bench765.6Not measuredOnly GLM 5 measured
LMArena Text: coding1461Not measuredOnly GLM 5 measured
LMArena Text: software and IT1460Not measuredOnly GLM 5 measured
LMArena WebDev: React1424Not measuredOnly GLM 5 measured
LMArena WebDev: HTML1450Not measuredOnly GLM 5 measured
Neither model has a result on the other 24 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for GLM 5 and GPT Chat Latest
BenchmarkGLM 5GPT Chat LatestLeader
AA Intelligence Index27.9Not measuredOnly GLM 5 measured
Epoch Capabilities Index145.8Not measuredOnly GLM 5 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Agentic work benchmarks for GLM 5 and GPT Chat Latest
BenchmarkGLM 5GPT Chat LatestLeader
Design Arena: agents building full-stack apps1123Not measuredOnly GLM 5 measured
Design Arena: agents building mobile apps1136Not measuredOnly GLM 5 measured
Design Arena: agents building Android apps1153Not measuredOnly GLM 5 measured
Design Arena: agents building Godot games1144Not measuredOnly GLM 5 measured
Design Arena: HTML slides1148Not measuredOnly GLM 5 measured
τ²-Bench Telecom98.2%Not measuredOnly GLM 5 measured
Vending-Bench 2$4,432Not measuredOnly GLM 5 measured
Neither model has a result on the other 32 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
Reasoning benchmarks for GLM 5 and GPT Chat Latest
BenchmarkGLM 5GPT Chat LatestLeader
GPQA Diamond83.3%Not measuredOnly GLM 5 measured
GPQA Diamond (Artificial Analysis run)82.0%Not measuredOnly GLM 5 measured
Humanity's Last Exam (Artificial Analysis run)29.3%Not measuredOnly GLM 5 measured
GPQA Diamond (Epoch AI run)87.8%Not measuredOnly GLM 5 measured
ARC-AGI44.7%Not measuredOnly GLM 5 measured
ARC-AGI-24.9%Not measuredOnly GLM 5 measured
SimpleBench53.2%Not measuredOnly GLM 5 measured
LMArena Text: hard prompts1452Not measuredOnly GLM 5 measured
LMArena Text: expert1454Not measuredOnly GLM 5 measured
LMArena Text: hard prompts in English1460Not measuredOnly GLM 5 measured
Neither model has a result on the other 11 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Math benchmarks for GLM 5 and GPT Chat Latest
BenchmarkGLM 5GPT Chat LatestLeader
AIME91.7%Not measuredOnly GLM 5 measured
OTIS Mock AIME80.0%Not measuredOnly GLM 5 measured
LMArena Text: math1440Not measuredOnly GLM 5 measured
LMArena Text: mathematical work1451Not measuredOnly GLM 5 measured
Neither model has a result on the other 9 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
Knowledge benchmarks for GLM 5 and GPT Chat Latest
BenchmarkGLM 5GPT Chat LatestLeader
MMLU Pro86.0%Not measuredOnly GLM 5 measured
LegalBench (Vals AI)84.1%Not measuredOnly GLM 5 measured
CaseLaw (Vals AI)52.5%Not measuredOnly GLM 5 measured
CorpFin (Vals AI)62.9%Not measuredOnly GLM 5 measured
TaxEval (Vals AI)70.0%Not measuredOnly GLM 5 measured
MedQA (Vals AI)94.3%Not measuredOnly GLM 5 measured
LMArena Text: science1460Not measuredOnly GLM 5 measured
LMArena Text: medicine and healthcare1455Not measuredOnly GLM 5 measured
LMArena Text: legal and government1453Not measuredOnly GLM 5 measured
LMArena Text: business and finance1430Not measuredOnly GLM 5 measured
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GLM 5 and GPT Chat Latest
BenchmarkGLM 5GPT Chat LatestLeader
Design Arena: websites1247Not measuredOnly GLM 5 measured
Design Arena: UI components1230Not measuredOnly GLM 5 measured
Design Arena: game dev1234Not measuredOnly GLM 5 measured
Design Arena: data viz1228Not measuredOnly GLM 5 measured
Design Arena: 3D1229Not measuredOnly GLM 5 measured
Design Arena: all code categories1246Not measuredOnly GLM 5 measured
Design Arena: SVG1168Not measuredOnly GLM 5 measured
Design Arena: ASCII art1146Not measuredOnly GLM 5 measured
Neither model has a result on the other 1 board here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index20 boards, none shared
General benchmarks for GLM 5 and GPT Chat Latest
BenchmarkGLM 5GPT Chat LatestLeader
IFBench72.3%Not measuredOnly GLM 5 measured
Long context reasoning (AA-LCR)75.7%Not measuredOnly GLM 5 measured
CL-bench18.7%Not measuredOnly GLM 5 measured
LMArena Text1446Not measuredOnly GLM 5 measured
LMArena Text: instruction following1428Not measuredOnly GLM 5 measured
LMArena Text: creative writing1438Not measuredOnly GLM 5 measured
LMArena Text: English1458Not measuredOnly GLM 5 measured
LMArena Text: non-English1430Not measuredOnly GLM 5 measured
LMArena Text: Chinese1511Not measuredOnly GLM 5 measured
LMArena Text: German1447Not measuredOnly GLM 5 measured
LMArena Text: Japanese1416Not measuredOnly GLM 5 measured
LMArena Text: Korean1423Not measuredOnly GLM 5 measured
LMArena Text: French1456Not measuredOnly GLM 5 measured
LMArena Text: Spanish1454Not measuredOnly GLM 5 measured
LMArena Text: Russian1436Not measuredOnly GLM 5 measured
LMArena Text: Polish1434Not measuredOnly GLM 5 measured
LMArena Text: multi-turn1456Not measuredOnly GLM 5 measured
LMArena Text: longer queries1446Not measuredOnly GLM 5 measured
LMArena Text: writing and language1437Not measuredOnly GLM 5 measured
LMArena Text: entertainment and media1426Not measuredOnly GLM 5 measured
Neither model has a result on the other 26 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5 and GPT Chat Latest
BenchmarkGLM 5GPT Chat LatestLeader
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 5$0.0026Fits in one request
GPT Chat Latest$0.020Fits in one request

GLM 5 costs less

Repository review

50K input + 3K output tokens

GLM 5$0.060Fits in one request
GPT Chat Latest$0.340Fits in one request

GLM 5 costs less

Agent loop

200K input + 10K output tokens

GLM 5$0.232Exceeds the context window
GPT Chat Latest$1.30Fits in one request

GLM 5 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 5 and GPT Chat Latest
SpecificationZ.ai GLM 5OpenAI GPT Chat Latest
MakerZ.aiOpenAI
WeightsOpen weightsNo public weights listed
ReleasedFebruary 11, 2026May 5, 2026
Context window205K400K
Input $/Mtok$1.00$5.00
Output $/Mtok$3.20$30.00
Coding index41.7Not benchmarked
Independent measurements behind it30
Cost per solved task$0.63Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5 better than GPT Chat Latest for coding?

Only GLM 5 has a coding score, 41.7 on the Vibe Coding Index; GPT Chat Latest has none, so the two cannot be ranked on coding. GPT Chat Latest has the edge where the context window is larger, 400K against 205K tokens.

Which is cheaper, GLM 5 or GPT Chat Latest?

GLM 5 costs $1.00 per million input tokens and $3.20 per million output tokens; GPT Chat Latest costs $5.00 and $30.00. Cost per solved task is known for GLM 5 only.

Which has the bigger context window, GLM 5 or GPT Chat Latest?

GPT Chat Latest, with 400K tokens against 205K.

How many benchmarks is the GLM 5 vs GPT Chat Latest comparison based on?

0 benchmarks have a result for both models as of October 21, 2026. GLM 5's coding index rests on 3 independent benchmarks and GPT Chat Latest's on 0. Where only one model has a result, the page says so instead of naming a winner.

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