GLM 4.7 vs o4 Mini
GLM 4.7 leads o4 Mini on the Artificial Analysis Intelligence Index, 22.2 to 16.7, and GLM 4.7 costs 2.0x less per output token ($2.20 vs $4.40 per million). Updated October 21, 2026; 62 benchmarks have a result for both.
GLM 4.7 or o4 Mini: which to pick
Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; o4 Mini has none, so the two cannot be ranked on coding.
Pick o4 Mini for work measured by Epoch Capabilities Index or Chess puzzles, where it leads.
- Shared results
- 62
- GLM 4.7 only
- 19
- o4 Mini only
- 36
- Like-for-like categories
- 8 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 16.7.
Same board, both measured
Reasoning
GPQA Diamond
GLM 4.7
GLM 4.7 leads, 80.0% against 74.5%.
Same board, both measured
Math
AIME
GLM 4.7
GLM 4.7 leads, 93.3% against 83.7%.
Same board, both measured
Knowledge
MMLU Pro
GLM 4.7
GLM 4.7 leads, 82.7% against 80.6%.
Same board, both measured
Web design
Design Arena: websites
GLM 4.7
GLM 4.7 leads, 1226 against 985.
Same board, both measured
Long documents
Listed context window
GLM 4.7
GLM 4.7 takes 205K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7
GLM 4.7 $0.0017, o4 Mini $0.0033 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, o4 Mini not benchmarked.
- Widest category
- Agentic work, 40.4 points apart on average over 1 shared benchmark.
- Widest single gap
- Design Arena: 3D: GLM 4.7 1191 against o4 Mini 854, 42.1 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $2.20 against o4 Mini $4.40 per million output tokens, 2.0x apart.
- Context window
- GLM 4.7 takes 205K tokens, o4 Mini 200K.
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 | o4 Mini | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 1 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 8 | 1 | ||
| Math | 6 | 1 | ||
| Knowledge | 8 | 4 | ||
| Web design | 6 | 0 | ||
| General | 18 | 2 | ||
| All shared boards | 53 | 9 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | GLM 4.7 | o4 Mini | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 72.0% | Only o4 Mini measured |
| SWE-bench Verified | Not measured | 64.6% | Only o4 Mini measured |
| SWE-bench Multimodal | Not measured | 33.9% | Only o4 Mini measured |
| 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% | 82.2% | GLM 4.7 |
| SWE-bench Verified (Vals AI) | 69.4% | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | 85.9% | GLM 4.7 |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | 15.2% | GLM 4.7 |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| GSO-Bench | Not measured | 3.6% | Only o4 Mini measured |
| WeirdML | Not measured | 52.6% | Only o4 Mini measured |
| ALE-Bench | 399.5 | Not measured | Only GLM 4.7 measured |
| LMArena Text: coding | 1454 | 1368 | GLM 4.7 |
| LMArena Text: software and IT | 1453 | 1367 | 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 |
| SciCode | Not measured | 10.8% | Only o4 Mini measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 4.7 | o4 Mini | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | 16.7 | GLM 4.7 |
| Epoch Capabilities Index | 143.6 | 145.5 | o4 Mini |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 | o4 Mini | 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% | 55.6% | GLM 4.7 |
| METR time horizons | Not measured | 63.9% | Only o4 Mini measured |
| Vending-Bench 2 | $2,377 | Not measured | Only GLM 4.7 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 53.2% | Only o4 Mini measured |
| BrowseComp-Plus | 64.7% | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index81
| Benchmark | GLM 4.7 | o4 Mini | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | 74.5% | GLM 4.7 |
| MMMU Pro (Vals AI) | Not measured | 79.7% | Only o4 Mini measured |
| GPQA Diamond (Artificial Analysis run) | 85.9% | 78.4% | GLM 4.7 |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 16.5% | GLM 4.7 |
| GPQA Diamond (Epoch AI run) | 83.3% | 79.6% | GLM 4.7 |
| ARC-AGI | Not measured | 58.7% | Only o4 Mini measured |
| ARC-AGI-2 | Not measured | 6.1% | Only o4 Mini measured |
| SimpleBench | 47.7% | 38.7% | GLM 4.7 |
| Chess puzzles | 21.0% | 22.0% | o4 Mini |
| Mystery game puzzles | Not measured | 5.0% | Only o4 Mini measured |
| VPCT | Not measured | 57.5% | Only o4 Mini measured |
| LMArena Text: hard prompts | 1443 | 1351 | GLM 4.7 |
| LMArena Text: expert | 1424 | 1343 | GLM 4.7 |
| LMArena Text: hard prompts in English | 1452 | 1366 | GLM 4.7 |
| LMArena Vision: diagrams | Not measured | 1197 | Only o4 Mini measured |
| LMArena Vision: homework | Not measured | 1234 | Only o4 Mini measured |
| Kagi LLM Benchmark | Not measured | 67.6% | Only o4 Mini measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index61
| Benchmark | GLM 4.7 | o4 Mini | Leader |
|---|---|---|---|
| AIME | 93.3% | 83.7% | GLM 4.7 |
| MATH 500 (Vals AI) | Not measured | 94.2% | Only o4 Mini measured |
| MGSM (Vals AI) | 88.2% | 93.4% | o4 Mini |
| AA Math Index | 95.0 | 90.7 | GLM 4.7 |
| AIME 2025 (Artificial Analysis run) | 95.0% | 90.7% | GLM 4.7 |
| OTIS Mock AIME | 83.3% | 81.7% | GLM 4.7 |
| FrontierMath, tiers 1 to 3 | Not measured | 36.1% | Only o4 Mini measured |
| FrontierMath, tier 4 | Not measured | 4.9% | Only o4 Mini measured |
| ProofBench | 6.0% | Not measured | Only GLM 4.7 measured |
| MATH level 5 | Not measured | 97.8% | Only o4 Mini measured |
| LMArena Text: math | 1424 | 1389 | GLM 4.7 |
| LMArena Text: mathematical work | 1411 | 1379 | GLM 4.7 |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index84
| Benchmark | GLM 4.7 | o4 Mini | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | 80.6% | GLM 4.7 |
| LegalBench (Vals AI) | 83.4% | 79.2% | GLM 4.7 |
| CaseLaw (Vals AI) | 54.9% | Not measured | Only GLM 4.7 measured |
| CorpFin (Vals AI) | 46.4% | 59.0% | o4 Mini |
| TaxEval (Vals AI) | 68.8% | 74.8% | o4 Mini |
| MortgageTax (Vals AI) | Not measured | 64.8% | Only o4 Mini measured |
| MedQA (Vals AI) | 93.7% | 96.0% | o4 Mini |
| MedCode (Vals AI) | 32.8% | 33.8% | o4 Mini |
| MMLU Pro (Artificial Analysis run) | 85.6% | 83.2% | GLM 4.7 |
| SimpleQA Verified | 32.2% | 19.6% | GLM 4.7 |
| GeoBench | Not measured | 64.0% | Only o4 Mini measured |
| LMArena Text: science | 1457 | 1354 | GLM 4.7 |
| LMArena Text: medicine and healthcare | 1469 | 1361 | GLM 4.7 |
| LMArena Text: legal and government | 1444 | 1350 | GLM 4.7 |
| LMArena Text: business and finance | 1429 | 1325 | GLM 4.7 |
| FACTS Benchmark Suite | Not measured | 28.1% | Only o4 Mini measured |
| Neither model has a result on the other 2 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 4.7 | o4 Mini | Leader |
|---|---|---|---|
| Design Arena: websites | 1226 | 985 | GLM 4.7 |
| Design Arena: UI components | 1199 | 983 | GLM 4.7 |
| Design Arena: game dev | 1189 | 1003 | GLM 4.7 |
| Design Arena: data viz | 1195 | 990 | GLM 4.7 |
| Design Arena: 3D | 1191 | 854 | GLM 4.7 |
| Design Arena: all code categories | 1217 | 976 | GLM 4.7 |
| 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 index182
| Benchmark | GLM 4.7 | o4 Mini | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 41.1% | Only o4 Mini measured |
| MedScribe (Vals AI) | 68.6% | 69.1% | o4 Mini |
| IFBench | 67.9% | 68.7% | o4 Mini |
| Long context reasoning (AA-LCR) | 71.0% | 61.0% | GLM 4.7 |
| DTBench | Not measured | 77.6% | Only o4 Mini measured |
| LMCA | Not measured | 26.5% | Only o4 Mini measured |
| Fiction.LiveBench | Not measured | 77.8% | Only o4 Mini measured |
| Creative writing (Lech Mazur) | Not measured | 75.0% | Only o4 Mini measured |
| 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 | 1353 | GLM 4.7 |
| LMArena Text: instruction following | 1412 | 1320 | GLM 4.7 |
| LMArena Text: creative writing | 1401 | 1294 | GLM 4.7 |
| LMArena Vision | Not measured | 1194 | Only o4 Mini measured |
| LMArena Text: English | 1453 | 1367 | GLM 4.7 |
| LMArena Text: non-English | 1417 | 1337 | GLM 4.7 |
| LMArena Text: Chinese | 1495 | 1354 | GLM 4.7 |
| LMArena Text: German | 1426 | 1336 | GLM 4.7 |
| LMArena Text: Japanese | 1439 | 1308 | GLM 4.7 |
| LMArena Text: Korean | 1399 | 1312 | GLM 4.7 |
| LMArena Text: French | 1432 | 1364 | GLM 4.7 |
| LMArena Text: Spanish | 1434 | 1346 | GLM 4.7 |
| LMArena Text: Russian | 1423 | 1334 | GLM 4.7 |
| LMArena Text: Polish | 1435 | 1358 | GLM 4.7 |
| LMArena Text: multi-turn | 1446 | 1350 | GLM 4.7 |
| LMArena Text: longer queries | 1432 | 1315 | GLM 4.7 |
| LMArena Text: writing and language | 1411 | 1316 | GLM 4.7 |
| LMArena Text: entertainment and media | 1400 | 1308 | GLM 4.7 |
| LMArena Vision: English | Not measured | 1200 | Only o4 Mini measured |
| LMArena Vision: Chinese | Not measured | 1218 | Only o4 Mini measured |
| LMArena Vision: reading text | Not measured | 1197 | Only o4 Mini measured |
| LMArena Vision: humor | Not measured | 1199 | Only o4 Mini measured |
| LMArena Vision: captioning | Not measured | 1204 | Only o4 Mini measured |
| LMArena Vision: recognizing things | Not measured | 1218 | Only o4 Mini measured |
| LMArena Vision: creative writing | Not measured | 1166 | Only o4 Mini measured |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 | o4 Mini | 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
GLM 4.7 costs less
Repository review
50K input + 3K output tokens
GLM 4.7 costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 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 | OpenAI o4 Mini |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | December 22, 2025 | April 16, 2025 |
| Context window | 205K | 200K |
| Input $/Mtok | $0.60 | $1.10 |
| Output $/Mtok | $2.20 | $4.40 |
| 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 o4 Mini for coding?
Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; o4 Mini has none, so the two cannot be ranked on coding. Pick o4 Mini for work measured by Epoch Capabilities Index or Chess puzzles, where it leads.
Which is cheaper, GLM 4.7 or o4 Mini?
GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; o4 Mini costs $1.10 and $4.40. Cost per solved task is known for GLM 4.7 only.
Which has the bigger context window, GLM 4.7 or o4 Mini?
GLM 4.7, with 205K tokens against 200K.
How many benchmarks is the GLM 4.7 vs o4 Mini comparison based on?
62 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 2 independent benchmarks and o4 Mini's on 0. Where only one model has a result, the page says so instead of naming a winner.