GLM 4.7 vs Step 3.5 Flash
GLM 4.7 leads Step 3.5 Flash on the Artificial Analysis Intelligence Index, 22.2 to 17.0, and Step 3.5 Flash costs 7.3x less per output token ($0.30 vs $2.20 per million). Updated October 21, 2026; 35 benchmarks have a result for both.
GLM 4.7 or Step 3.5 Flash: which to pick
Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Step 3.5 Flash has none, so the two cannot be ranked on coding.
Pick Step 3.5 Flash for output tokens 7.3x cheaper ($0.30 vs $2.20 per million) and a larger context window (262K against 205K tokens).
- Shared results
- 35
- GLM 4.7 only
- 46
- Step 3.5 Flash only
- 0
- Like-for-like categories
- 7 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only GLM 4.7 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.7
GLM 4.7 leads, 22.2 against 17.0.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 4.7 has a GPQA Diamond result, 80.0%.
Directional only
Math
AIME
No call
Only GLM 4.7 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.7 has a MMLU Pro result, 82.7%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.7 has a Design Arena: websites result, 1226.
Directional only
Long documents
Listed context window
Step 3.5 Flash
Step 3.5 Flash takes 262K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Step 3.5 Flash
GLM 4.7 $0.0017, Step 3.5 Flash $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 4.7 has the number, $0.29.
Directional only
At a glance
- Coding index
- GLM 4.7 55.1 from 2 benchmarks, Step 3.5 Flash not benchmarked.
- Widest category
- Knowledge, 7.3 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena Text: Japanese: GLM 4.7 1439 against Step 3.5 Flash 1353, 14.3 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $2.20 against Step 3.5 Flash $0.30 per million output tokens, 7.3x apart.
- Context window
- GLM 4.7 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.
| Category | GLM 4.7 | Bars | Step 3.5 Flash | Ties |
|---|---|---|---|---|
| Coding | 2 | 1 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 1 | 1 | ||
| Knowledge | 4 | 0 | ||
| General | 19 | 0 | ||
| All shared boards | 33 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index21
| Benchmark | GLM 4.7 | Step 3.5 Flash | Leader |
|---|---|---|---|
| LMArena WebDev | 1435 | Not measured | Only GLM 4.7 measured |
| AA Coding Index | 45.3 | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Vals AI) | 82.2% | Not measured | Only GLM 4.7 measured |
| SWE-bench Verified (Vals AI) | 69.4% | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GLM 4.7 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | 32.6% | Step 3.5 Flash |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| ALE-Bench | 399.5 | Not measured | Only GLM 4.7 measured |
| LMArena Text: coding | 1454 | 1436 | GLM 4.7 |
| LMArena Text: software and IT | 1453 | 1434 | GLM 4.7 |
| LMArena WebDev: React | 1420 | Not measured | Only GLM 4.7 measured |
| LMArena WebDev: HTML | 1447 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 | Step 3.5 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | 17.0 | GLM 4.7 |
| Epoch Capabilities Index | 143.6 | Not measured | Only GLM 4.7 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 | Step 3.5 Flash | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1050 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building mobile apps | 1105 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Android apps | 1112 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Godot games | 1058 | Not measured | Only GLM 4.7 measured |
| τ²-Bench Telecom | 95.9% | 94.4% | GLM 4.7 |
| Vending-Bench 2 | $2,377 | Not measured | Only GLM 4.7 measured |
| BrowseComp-Plus | 64.7% | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.7 | Step 3.5 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | Not measured | Only GLM 4.7 measured |
| GPQA Diamond (Artificial Analysis run) | 85.9% | 83.1% | GLM 4.7 |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 24.5% | GLM 4.7 |
| GPQA Diamond (Epoch AI run) | 83.3% | Not measured | Only GLM 4.7 measured |
| SimpleBench | 47.7% | Not measured | Only GLM 4.7 measured |
| Chess puzzles | 21.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: hard prompts | 1443 | 1411 | GLM 4.7 |
| LMArena Text: expert | 1424 | 1421 | GLM 4.7 |
| LMArena Text: hard prompts in English | 1452 | 1421 | GLM 4.7 |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 4.7 | Step 3.5 Flash | Leader |
|---|---|---|---|
| AIME | 93.3% | Not measured | Only GLM 4.7 measured |
| MGSM (Vals AI) | 88.2% | Not measured | Only GLM 4.7 measured |
| AA Math Index | 95.0 | Not measured | Only GLM 4.7 measured |
| AIME 2025 (Artificial Analysis run) | 95.0% | Not measured | Only GLM 4.7 measured |
| OTIS Mock AIME | 83.3% | Not measured | Only GLM 4.7 measured |
| ProofBench | 6.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: math | 1424 | 1408 | GLM 4.7 |
| LMArena Text: mathematical work | 1411 | 1421 | Step 3.5 Flash |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.7 | Step 3.5 Flash | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | Not measured | Only GLM 4.7 measured |
| LegalBench (Vals AI) | 83.4% | Not measured | Only GLM 4.7 measured |
| CaseLaw (Vals AI) | 54.9% | Not measured | Only GLM 4.7 measured |
| CorpFin (Vals AI) | 46.4% | Not measured | Only GLM 4.7 measured |
| TaxEval (Vals AI) | 68.8% | Not measured | Only GLM 4.7 measured |
| MedQA (Vals AI) | 93.7% | Not measured | Only GLM 4.7 measured |
| MedCode (Vals AI) | 32.8% | Not measured | Only GLM 4.7 measured |
| MMLU Pro (Artificial Analysis run) | 85.6% | Not measured | Only GLM 4.7 measured |
| SimpleQA Verified | 32.2% | Not measured | Only GLM 4.7 measured |
| LMArena Text: science | 1457 | 1415 | GLM 4.7 |
| LMArena Text: medicine and healthcare | 1469 | 1408 | GLM 4.7 |
| LMArena Text: legal and government | 1444 | 1408 | GLM 4.7 |
| LMArena Text: business and finance | 1429 | 1394 | GLM 4.7 |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 4.7 | Step 3.5 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1226 | Not measured | Only GLM 4.7 measured |
| Design Arena: UI components | 1199 | Not measured | Only GLM 4.7 measured |
| Design Arena: game dev | 1189 | Not measured | Only GLM 4.7 measured |
| Design Arena: data viz | 1195 | Not measured | Only GLM 4.7 measured |
| Design Arena: 3D | 1191 | Not measured | Only GLM 4.7 measured |
| Design Arena: all code categories | 1217 | Not measured | Only GLM 4.7 measured |
| Design Arena: SVG | 1143 | Not measured | Only GLM 4.7 measured |
| Design Arena: ASCII art | 1165 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
| Benchmark | GLM 4.7 | Step 3.5 Flash | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 68.6% | Not measured | Only GLM 4.7 measured |
| IFBench | 67.9% | 66.5% | GLM 4.7 |
| Long context reasoning (AA-LCR) | 71.0% | 63.0% | GLM 4.7 |
| CL-bench | 15.9% | Not measured | Only GLM 4.7 measured |
| CL-bench Life | 10.9% | Not measured | Only GLM 4.7 measured |
| LMArena Text | 1435 | 1403 | GLM 4.7 |
| LMArena Text: instruction following | 1412 | 1385 | GLM 4.7 |
| LMArena Text: creative writing | 1401 | 1357 | GLM 4.7 |
| LMArena Text: English | 1453 | 1417 | GLM 4.7 |
| LMArena Text: non-English | 1417 | 1385 | GLM 4.7 |
| LMArena Text: Chinese | 1495 | 1446 | GLM 4.7 |
| LMArena Text: German | 1426 | 1405 | GLM 4.7 |
| LMArena Text: Japanese | 1439 | 1353 | GLM 4.7 |
| LMArena Text: Korean | 1399 | 1351 | GLM 4.7 |
| LMArena Text: French | 1432 | 1421 | GLM 4.7 |
| LMArena Text: Spanish | 1434 | 1419 | GLM 4.7 |
| LMArena Text: Russian | 1423 | 1385 | GLM 4.7 |
| LMArena Text: Polish | 1435 | 1389 | GLM 4.7 |
| LMArena Text: multi-turn | 1446 | 1404 | GLM 4.7 |
| LMArena Text: longer queries | 1432 | 1402 | GLM 4.7 |
| LMArena Text: writing and language | 1411 | 1372 | GLM 4.7 |
| LMArena Text: entertainment and media | 1400 | 1360 | GLM 4.7 |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 | 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 4.7 | StepFun Step 3.5 Flash |
|---|---|---|
| Maker | Z.ai | StepFun |
| Weights | Open weights | Open weights |
| Released | December 22, 2025 | January 29, 2026 |
| Context window | 205K | 262K |
| Input $/Mtok | $0.60 | $0.10 |
| Output $/Mtok | $2.20 | $0.30 |
| Coding index | 55.1 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | $0.29 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than Step 3.5 Flash for coding?
Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Step 3.5 Flash has none, so the two cannot be ranked on coding. Pick Step 3.5 Flash for output tokens 7.3x cheaper ($0.30 vs $2.20 per million) and a larger context window (262K against 205K tokens).
Which is cheaper, GLM 4.7 or Step 3.5 Flash?
GLM 4.7 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 GLM 4.7 only.
Which has the bigger context window, GLM 4.7 or Step 3.5 Flash?
Step 3.5 Flash, with 262K tokens against 205K.
How many benchmarks is the GLM 4.7 vs Step 3.5 Flash comparison based on?
35 benchmarks have a result for both models as of October 21, 2026. GLM 4.7'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.