GLM 4.5 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.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
GLM 4.5 has the edge where what matters is Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.
- Shared results
- 35
- GLM 4.5 only
- 25
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Step 3.5 Flash
Step 3.5 Flash leads, 17.0 against 12.8.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 4.5 has a GPQA Diamond result, 72.2%.
Directional only
Math
AIME
No call
Only GLM 4.5 has a AIME result, 86.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.5 has a MMLU Pro result, 81.2%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.5 has a Design Arena: websites result, 1170.
Directional only
Long documents
Listed context window
Step 3.5 Flash
Step 3.5 Flash takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Step 3.5 Flash
GLM 4.5 $0.0017, Step 3.5 Flash $0.0003 per turn.
List prices
At a glance
- Coding index
- GLM 4.5 not benchmarked, Step 3.5 Flash not benchmarked.
- Widest category
- Agentic work, 51.5 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 4.5 43.0% against Step 3.5 Flash 94.4%, 51.5 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 $2.20 against Step 3.5 Flash $0.30 per million output tokens, 7.3x apart.
- Context window
- GLM 4.5 takes 131K 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.
| Category | GLM 4.5 | Bars | Step 3.5 Flash | Ties |
|---|---|---|---|---|
| Coding | 1 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 3 | 2 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 16 | 3 | ||
| All shared boards | 26 | 9 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index12
| Benchmark | GLM 4.5 | Step 3.5 Flash | Leader |
|---|---|---|---|
| SWE-bench Verified | 64.2% | Not measured | Only GLM 4.5 measured |
| LiveCodeBench (Vals AI) | 67.5% | Not measured | Only GLM 4.5 measured |
| LiveCodeBench (Artificial Analysis run) | 73.8% | Not measured | Only GLM 4.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | 32.6% | Step 3.5 Flash |
| WeirdML | 40.6% | Not measured | Only GLM 4.5 measured |
| ALE-Bench | 344.8 | Not measured | Only GLM 4.5 measured |
| LMArena Text: coding | 1434 | 1436 | Step 3.5 Flash |
| LMArena Text: software and IT | 1440 | 1434 | GLM 4.5 |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | Step 3.5 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.8 | 17.0 | Step 3.5 Flash |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | Step 3.5 Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 43.0% | 94.4% | Step 3.5 Flash |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | GLM 4.5 | Step 3.5 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | Not measured | Only GLM 4.5 measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | 83.1% | Step 3.5 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 13.0% | 24.5% | Step 3.5 Flash |
| LMArena Text: hard prompts | 1429 | 1411 | GLM 4.5 |
| LMArena Text: expert | 1433 | 1421 | GLM 4.5 |
| LMArena Text: hard prompts in English | 1429 | 1421 | GLM 4.5 |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.5 | Step 3.5 Flash | Leader |
|---|---|---|---|
| AIME | 86.7% | Not measured | Only GLM 4.5 measured |
| MATH 500 (Vals AI) | 94.0% | Not measured | Only GLM 4.5 measured |
| MGSM (Vals AI) | 90.8% | Not measured | Only GLM 4.5 measured |
| AA Math Index | 73.7 | Not measured | Only GLM 4.5 measured |
| AIME 2025 (Artificial Analysis run) | 73.7% | Not measured | Only GLM 4.5 measured |
| LMArena Text: math | 1427 | 1408 | GLM 4.5 |
| LMArena Text: mathematical work | 1430 | 1421 | GLM 4.5 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.5 | Step 3.5 Flash | Leader |
|---|---|---|---|
| MMLU Pro | 81.2% | Not measured | Only GLM 4.5 measured |
| LegalBench (Vals AI) | 75.6% | Not measured | Only GLM 4.5 measured |
| CorpFin (Vals AI) | 61.0% | Not measured | Only GLM 4.5 measured |
| TaxEval (Vals AI) | 72.4% | Not measured | Only GLM 4.5 measured |
| MedQA (Vals AI) | 90.0% | Not measured | Only GLM 4.5 measured |
| MMLU Pro (Artificial Analysis run) | 83.5% | Not measured | Only GLM 4.5 measured |
| LMArena Text: science | 1444 | 1415 | GLM 4.5 |
| LMArena Text: medicine and healthcare | 1455 | 1408 | GLM 4.5 |
| LMArena Text: legal and government | 1438 | 1408 | GLM 4.5 |
| LMArena Text: business and finance | 1420 | 1394 | GLM 4.5 |
| 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
| Benchmark | GLM 4.5 | Step 3.5 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1170 | Not measured | Only GLM 4.5 measured |
| Design Arena: UI components | 1144 | Not measured | Only GLM 4.5 measured |
| Design Arena: game dev | 1145 | Not measured | Only GLM 4.5 measured |
| Design Arena: data viz | 1159 | Not measured | Only GLM 4.5 measured |
| Design Arena: 3D | 1170 | Not measured | Only GLM 4.5 measured |
| Design Arena: all code categories | 1165 | Not measured | Only GLM 4.5 measured |
| Design Arena: SVG | 1096 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index163
| Benchmark | GLM 4.5 | Step 3.5 Flash | Leader |
|---|---|---|---|
| IFBench | 44.1% | 66.5% | Step 3.5 Flash |
| Long context reasoning (AA-LCR) | 52.7% | 63.0% | Step 3.5 Flash |
| Creative writing (Lech Mazur) | 73.4% | Not measured | Only GLM 4.5 measured |
| LMArena Text | 1430 | 1403 | GLM 4.5 |
| LMArena Text: instruction following | 1404 | 1385 | GLM 4.5 |
| LMArena Text: creative writing | 1395 | 1357 | GLM 4.5 |
| LMArena Text: English | 1437 | 1417 | GLM 4.5 |
| LMArena Text: non-English | 1417 | 1385 | GLM 4.5 |
| LMArena Text: Chinese | 1465 | 1446 | GLM 4.5 |
| LMArena Text: German | 1407 | 1405 | GLM 4.5 |
| LMArena Text: Japanese | 1415 | 1353 | GLM 4.5 |
| LMArena Text: Korean | 1380 | 1351 | GLM 4.5 |
| LMArena Text: French | 1418 | 1421 | Step 3.5 Flash |
| LMArena Text: Spanish | 1454 | 1419 | GLM 4.5 |
| LMArena Text: Russian | 1413 | 1385 | GLM 4.5 |
| LMArena Text: Polish | 1422 | 1389 | GLM 4.5 |
| LMArena Text: multi-turn | 1415 | 1404 | GLM 4.5 |
| LMArena Text: longer queries | 1412 | 1402 | GLM 4.5 |
| LMArena Text: writing and language | 1403 | 1372 | GLM 4.5 |
| LMArena Text: entertainment and media | 1404 | 1360 | GLM 4.5 |
| 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
| Benchmark | GLM 4.5 | Step 3.5 Flash | Leader |
|---|---|---|---|
| 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
Step 3.5 Flash costs less
Repository review
50K input + 3K output tokens
Step 3.5 Flash costs less
Agent loop
200K input + 10K output tokens
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.
| Specification | Z.ai GLM 4.5 | StepFun Step 3.5 Flash |
|---|---|---|
| Maker | Z.ai | StepFun |
| Weights | Open weights | Open weights |
| Released | July 25, 2025 | January 29, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.60 | $0.10 |
| Output $/Mtok | $2.20 | $0.30 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5 better than Step 3.5 Flash for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 4.5 has the edge where what matters is Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.
Which is cheaper, GLM 4.5 or Step 3.5 Flash?
GLM 4.5 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 4.5 or Step 3.5 Flash?
Step 3.5 Flash, with 262K tokens against 131K.
How many benchmarks is the GLM 4.5 vs Step 3.5 Flash comparison based on?
35 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 0 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.