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GLM 4.6 vs Step 3.5 Flash

Updated October 21, 2026. 35 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.6 Replace Step 3.5 Flash
GLM 4.6Z.ai
Coding
8.8
Overall
18.5
Output $/M
$2.20
29
Step 3.5 FlashStepFun
Coding
No data
Overall
17.0
Output $/M
$0.30
6
vs boards won of 35 shared

Decision reading

Only GLM 4.6 has a coding score, 8.8 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 7.3x less, and where the context window is larger, 262K against 205K tokens.

Model AGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved taskModel BStep 3.5 FlashStepFunNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
35
GLM 4.6 only
31
Step 3.5 Flash only
0
Like-for-like categories
7 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 4.6 8.8 from 2 benchmarks, Step 3.5 Flash not benchmarked.
Widest category
Agentic work, 17.5 points apart on average over 1 shared benchmark.
Widest single gap
IFBench: GLM 4.6 43.4% against Step 3.5 Flash 66.5%, 23.1 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against Step 3.5 Flash $0.30 per million output tokens, 7.3x apart.
Context window
GLM 4.6 takes 205K 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
CategoryGLM 4.6BarsStep 3.5 FlashTies
Coding21
Intelligence10
Agentic work01
Reasoning32
Math20
Knowledge40
General172
All shared boards296
CodingLike-for-likeSix of these boards feed the Vibe Coding Index21
Coding benchmarks for GLM 4.6 and Step 3.5 Flash
BenchmarkGLM 4.6Step 3.5 FlashLeader
SWE-bench Verified68.2%Not measuredOnly GLM 4.6 measured
LMArena WebDev1341Not measuredOnly GLM 4.6 measured
AA Coding Index45.8Not measuredOnly GLM 4.6 measured
LiveCodeBench (Vals AI)81.0%Not measuredOnly GLM 4.6 measured
Vibe Code Bench (Vals AI)3.1%Not measuredOnly GLM 4.6 measured
LiveCodeBench (Artificial Analysis run)69.5%Not measuredOnly GLM 4.6 measured
Terminal-Bench Hard (Artificial Analysis run)28.8%32.6%Step 3.5 Flash
Terminal-Bench24.5%Not measuredOnly GLM 4.6 measured
ALE-Bench340.8Not measuredOnly GLM 4.6 measured
LMArena Text: coding14491436GLM 4.6
LMArena Text: software and IT14551434GLM 4.6
LMArena WebDev: HTML1350Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.6 and Step 3.5 Flash
BenchmarkGLM 4.6Step 3.5 FlashLeader
AA Intelligence Index18.517.0GLM 4.6
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 GLM 4.6 and Step 3.5 Flash
BenchmarkGLM 4.6Step 3.5 FlashLeader
Design Arena: agents building full-stack apps1029Not measuredOnly GLM 4.6 measured
Design Arena: agents building mobile apps1096Not measuredOnly GLM 4.6 measured
Design Arena: agents building Android apps1091Not measuredOnly GLM 4.6 measured
Design Arena: agents building Godot games1180Not measuredOnly GLM 4.6 measured
τ²-Bench Telecom76.9%94.4%Step 3.5 Flash
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index32
Reasoning benchmarks for GLM 4.6 and Step 3.5 Flash
BenchmarkGLM 4.6Step 3.5 FlashLeader
GPQA Diamond74.5%Not measuredOnly GLM 4.6 measured
GPQA Diamond (Artificial Analysis run)78.0%83.1%Step 3.5 Flash
Humanity's Last Exam (Artificial Analysis run)14.5%24.5%Step 3.5 Flash
LMArena Text: hard prompts14401411GLM 4.6
LMArena Text: expert14321421GLM 4.6
LMArena Text: hard prompts in English14451421GLM 4.6
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for GLM 4.6 and Step 3.5 Flash
BenchmarkGLM 4.6Step 3.5 FlashLeader
AIME92.7%Not measuredOnly GLM 4.6 measured
MGSM (Vals AI)89.8%Not measuredOnly GLM 4.6 measured
AA Math Index86.0Not measuredOnly GLM 4.6 measured
AIME 2025 (Artificial Analysis run)86.0%Not measuredOnly GLM 4.6 measured
LMArena Text: math14321408GLM 4.6
LMArena Text: mathematical work14471421GLM 4.6
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 4.6 and Step 3.5 Flash
BenchmarkGLM 4.6Step 3.5 FlashLeader
MMLU Pro82.2%Not measuredOnly GLM 4.6 measured
LegalBench (Vals AI)79.6%Not measuredOnly GLM 4.6 measured
CorpFin (Vals AI)56.8%Not measuredOnly GLM 4.6 measured
TaxEval (Vals AI)66.2%Not measuredOnly GLM 4.6 measured
MedQA (Vals AI)92.2%Not measuredOnly GLM 4.6 measured
MMLU Pro (Artificial Analysis run)82.9%Not measuredOnly GLM 4.6 measured
LMArena Text: science14561415GLM 4.6
LMArena Text: medicine and healthcare14551408GLM 4.6
LMArena Text: legal and government14481408GLM 4.6
LMArena Text: business and finance14161394GLM 4.6
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.6 and Step 3.5 Flash
BenchmarkGLM 4.6Step 3.5 FlashLeader
Design Arena: websites1174Not measuredOnly GLM 4.6 measured
Design Arena: UI components1148Not measuredOnly GLM 4.6 measured
Design Arena: game dev1151Not measuredOnly GLM 4.6 measured
Design Arena: data viz1161Not measuredOnly GLM 4.6 measured
Design Arena: 3D1129Not measuredOnly GLM 4.6 measured
Design Arena: all code categories1166Not measuredOnly GLM 4.6 measured
Design Arena: SVG1109Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index172
General benchmarks for GLM 4.6 and Step 3.5 Flash
BenchmarkGLM 4.6Step 3.5 FlashLeader
IFBench43.4%66.5%Step 3.5 Flash
Long context reasoning (AA-LCR)54.0%63.0%Step 3.5 Flash
LMArena Text14401403GLM 4.6
LMArena Text: instruction following14101385GLM 4.6
LMArena Text: creative writing14121357GLM 4.6
LMArena Text: English14491417GLM 4.6
LMArena Text: non-English14261385GLM 4.6
LMArena Text: Chinese14991446GLM 4.6
LMArena Text: German14471405GLM 4.6
LMArena Text: Japanese13931353GLM 4.6
LMArena Text: Korean14001351GLM 4.6
LMArena Text: French14591421GLM 4.6
LMArena Text: Spanish14361419GLM 4.6
LMArena Text: Russian14181385GLM 4.6
LMArena Text: Polish14471389GLM 4.6
LMArena Text: multi-turn14271404GLM 4.6
LMArena Text: longer queries14221402GLM 4.6
LMArena Text: writing and language14151372GLM 4.6
LMArena Text: entertainment and media14091360GLM 4.6
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 GLM 4.6 and Step 3.5 Flash
BenchmarkGLM 4.6Step 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

GLM 4.6$0.0017Fits 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

GLM 4.6$0.037Fits 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

GLM 4.6$0.142Exceeds the context window
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 GLM 4.6 and Step 3.5 Flash
SpecificationZ.ai GLM 4.6StepFun Step 3.5 Flash
MakerZ.aiStepFun
WeightsOpen weightsOpen weights
ReleasedSeptember 30, 2025January 29, 2026
Context window205K262K
Input $/Mtok$0.60$0.10
Output $/Mtok$2.20$0.30
Coding index8.8Not benchmarked
Independent measurements behind it20
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than Step 3.5 Flash for coding?

Only GLM 4.6 has a coding score, 8.8 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 7.3x less, and where the context window is larger, 262K against 205K tokens.

Which is cheaper, GLM 4.6 or Step 3.5 Flash?

GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; Step 3.5 Flash costs $0.10 and $0.30. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.6 or Step 3.5 Flash?

Step 3.5 Flash, with 262K tokens against 205K.

How many benchmarks is the GLM 4.6 vs Step 3.5 Flash comparison based on?

35 benchmarks have a result for both models as of October 21, 2026. GLM 4.6's coding index rests on 2 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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