GLM 4.5 vs o4 Mini
Updated October 21, 2026. 58 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick o4 Mini for coding: it scores 68.6 on the Vibe Coding Index against 64.2 for GLM 4.5.
Pick GLM 4.5 when output tokens cost 2.0x less, or when what matters is Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.
- Shared results
- 58
- GLM 4.5 only
- 2
- o4 Mini only
- 36
- Like-for-like categories
- 8 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, four public coding benchmarks
o4 Mini
o4 Mini scores 68.6 against 64.2.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
o4 Mini
o4 Mini leads, 16.7 against 12.8.
Same board, both measured
Reasoning
GPQA Diamond
o4 Mini
o4 Mini leads, 74.5% against 72.2%.
Same board, both measured
Math
AIME
GLM 4.5
GLM 4.5 leads, 86.7% against 83.7%.
Same board, both measured
Knowledge
MMLU Pro
GLM 4.5
GLM 4.5 leads, 81.2% against 80.6%.
Same board, both measured
Web design
Design Arena: websites
GLM 4.5
GLM 4.5 leads, 1170 against 985.
Same board, both measured
Long documents
Listed context window
o4 Mini
o4 Mini takes 200K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5
GLM 4.5 $0.0017, o4 Mini $0.0033 per turn.
List prices
At a glance
- Coding index
- GLM 4.5 64.2 from 1 benchmark, o4 Mini 68.6 from 2 benchmarks.
- Widest category
- Web design, 24.2 points apart on average over 6 shared benchmarks.
- Widest single gap
- Design Arena: 3D: GLM 4.5 1170 against o4 Mini 854, 39.5 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 $2.20 against o4 Mini $4.40 per million output tokens, 2.0x apart.
- Context window
- GLM 4.5 takes 131K 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 4.5 | Bars | o4 Mini | Ties |
|---|---|---|---|---|
| Coding | 3 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 3 | 3 | ||
| Math | 3 | 4 | ||
| Knowledge | 7 | 3 | ||
| Web design | 6 | 0 | ||
| General | 17 | 3 | ||
| All shared boards | 39 | 19 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index34
| Benchmark | GLM 4.5 | o4 Mini | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 72.0% | Only o4 Mini measured |
| SWE-bench Verified | 64.2% | 64.6% | o4 Mini |
| SWE-bench Multimodal | Not measured | 33.9% | Only o4 Mini measured |
| LiveCodeBench (Vals AI) | 67.5% | 82.2% | o4 Mini |
| LiveCodeBench (Artificial Analysis run) | 73.8% | 85.9% | o4 Mini |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | 15.2% | GLM 4.5 |
| GSO-Bench | Not measured | 3.6% | Only o4 Mini measured |
| WeirdML | 40.6% | 52.6% | o4 Mini |
| ALE-Bench | 344.8 | Not measured | Only GLM 4.5 measured |
| LMArena Text: coding | 1434 | 1368 | GLM 4.5 |
| LMArena Text: software and IT | 1440 | 1367 | GLM 4.5 |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | o4 Mini | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.8 | 16.7 | o4 Mini |
| Epoch Capabilities Index | Not measured | 145.5 | Only o4 Mini measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | o4 Mini | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 43.0% | 55.6% | o4 Mini |
| METR time horizons | Not measured | 63.9% | Only o4 Mini measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index33
| Benchmark | GLM 4.5 | o4 Mini | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | 74.5% | o4 Mini |
| MMMU Pro (Vals AI) | Not measured | 79.7% | Only o4 Mini measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | 78.4% | o4 Mini |
| Humanity's Last Exam (Artificial Analysis run) | 13.0% | 16.5% | o4 Mini |
| GPQA Diamond (Epoch AI run) | Not measured | 79.6% | Only o4 Mini measured |
| ARC-AGI | Not measured | 58.7% | Only o4 Mini measured |
| ARC-AGI-2 | Not measured | 6.1% | Only o4 Mini measured |
| SimpleBench | Not measured | 38.7% | Only o4 Mini measured |
| Chess puzzles | Not measured | 22.0% | Only o4 Mini measured |
| Mystery game puzzles | Not measured | 5.0% | Only o4 Mini measured |
| VPCT | Not measured | 57.5% | Only o4 Mini measured |
| LMArena Text: hard prompts | 1429 | 1351 | GLM 4.5 |
| LMArena Text: expert | 1433 | 1343 | GLM 4.5 |
| LMArena Text: hard prompts in English | 1429 | 1366 | GLM 4.5 |
| LMArena Vision: diagrams | Not measured | 1197 | Only o4 Mini measured |
| LMArena Vision: homework | Not measured | 1234 | 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 index34
| Benchmark | GLM 4.5 | o4 Mini | Leader |
|---|---|---|---|
| AIME | 86.7% | 83.7% | GLM 4.5 |
| MATH 500 (Vals AI) | 94.0% | 94.2% | o4 Mini |
| MGSM (Vals AI) | 90.8% | 93.4% | o4 Mini |
| AA Math Index | 73.7 | 90.7 | o4 Mini |
| AIME 2025 (Artificial Analysis run) | 73.7% | 90.7% | o4 Mini |
| OTIS Mock AIME | Not measured | 81.7% | Only o4 Mini measured |
| FrontierMath, tiers 1 to 3 | Not measured | 36.1% | Only o4 Mini measured |
| FrontierMath, tier 4 | Not measured | 4.9% | Only o4 Mini measured |
| MATH level 5 | Not measured | 97.8% | Only o4 Mini measured |
| LMArena Text: math | 1427 | 1389 | GLM 4.5 |
| LMArena Text: mathematical work | 1430 | 1379 | GLM 4.5 |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index73
| Benchmark | GLM 4.5 | o4 Mini | Leader |
|---|---|---|---|
| MMLU Pro | 81.2% | 80.6% | GLM 4.5 |
| LegalBench (Vals AI) | 75.6% | 79.2% | o4 Mini |
| CorpFin (Vals AI) | 61.0% | 59.0% | GLM 4.5 |
| TaxEval (Vals AI) | 72.4% | 74.8% | o4 Mini |
| MortgageTax (Vals AI) | Not measured | 64.8% | Only o4 Mini measured |
| MedQA (Vals AI) | 90.0% | 96.0% | o4 Mini |
| MedCode (Vals AI) | Not measured | 33.8% | Only o4 Mini measured |
| MMLU Pro (Artificial Analysis run) | 83.5% | 83.2% | GLM 4.5 |
| SimpleQA Verified | Not measured | 19.6% | Only o4 Mini measured |
| GeoBench | Not measured | 64.0% | Only o4 Mini measured |
| LMArena Text: science | 1444 | 1354 | GLM 4.5 |
| LMArena Text: medicine and healthcare | 1455 | 1361 | GLM 4.5 |
| LMArena Text: legal and government | 1438 | 1350 | GLM 4.5 |
| LMArena Text: business and finance | 1420 | 1325 | GLM 4.5 |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 4.5 | o4 Mini | Leader |
|---|---|---|---|
| Design Arena: websites | 1170 | 985 | GLM 4.5 |
| Design Arena: UI components | 1144 | 983 | GLM 4.5 |
| Design Arena: game dev | 1145 | 1004 | GLM 4.5 |
| Design Arena: data viz | 1159 | 990 | GLM 4.5 |
| Design Arena: 3D | 1170 | 854 | GLM 4.5 |
| Design Arena: all code categories | 1165 | 977 | GLM 4.5 |
| 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 index173
| Benchmark | GLM 4.5 | o4 Mini | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 41.1% | Only o4 Mini measured |
| MedScribe (Vals AI) | Not measured | 69.1% | Only o4 Mini measured |
| IFBench | 44.1% | 68.7% | o4 Mini |
| Long context reasoning (AA-LCR) | 52.7% | 61.0% | o4 Mini |
| 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) | 73.4% | 75.0% | o4 Mini |
| LMArena Text | 1430 | 1353 | GLM 4.5 |
| LMArena Text: instruction following | 1404 | 1320 | GLM 4.5 |
| LMArena Text: creative writing | 1395 | 1294 | GLM 4.5 |
| LMArena Vision | Not measured | 1194 | Only o4 Mini measured |
| LMArena Text: English | 1437 | 1367 | GLM 4.5 |
| LMArena Text: non-English | 1417 | 1337 | GLM 4.5 |
| LMArena Text: Chinese | 1465 | 1354 | GLM 4.5 |
| LMArena Text: German | 1407 | 1336 | GLM 4.5 |
| LMArena Text: Japanese | 1415 | 1308 | GLM 4.5 |
| LMArena Text: Korean | 1380 | 1312 | GLM 4.5 |
| LMArena Text: French | 1418 | 1364 | GLM 4.5 |
| LMArena Text: Spanish | 1454 | 1346 | GLM 4.5 |
| LMArena Text: Russian | 1413 | 1334 | GLM 4.5 |
| LMArena Text: Polish | 1422 | 1358 | GLM 4.5 |
| LMArena Text: multi-turn | 1415 | 1350 | GLM 4.5 |
| LMArena Text: longer queries | 1412 | 1315 | GLM 4.5 |
| LMArena Text: writing and language | 1403 | 1316 | GLM 4.5 |
| LMArena Text: entertainment and media | 1404 | 1308 | GLM 4.5 |
| 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 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5 | 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 4.5 costs less
Repository review
50K input + 3K output tokens
GLM 4.5 costs less
Agent loop
200K input + 10K output tokens
GLM 4.5 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.
| Specification | Z.ai GLM 4.5 | OpenAI o4 Mini |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | July 25, 2025 | April 16, 2025 |
| Context window | 131K | 200K |
| Input $/Mtok | $0.60 | $1.10 |
| Output $/Mtok | $2.20 | $4.40 |
| Coding index | 64.2 | 68.6 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5 better than o4 Mini for coding?
Pick o4 Mini for coding: it scores 68.6 on the Vibe Coding Index against 64.2 for GLM 4.5. Pick GLM 4.5 when output tokens cost 2.0x less, or when what matters is Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.
Which is cheaper, GLM 4.5 or o4 Mini?
GLM 4.5 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 neither of them.
Which has the bigger context window, GLM 4.5 or o4 Mini?
o4 Mini, with 200K tokens against 131K.
How many benchmarks is the GLM 4.5 vs o4 Mini comparison based on?
58 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 1 independent benchmark and o4 Mini's on 2. Where only one model has a result, the page says so instead of naming a winner.