GLM 5.3 Flash vs o4 Mini
GLM 5.3 Flash leads o4 Mini on the Artificial Analysis Intelligence Index, 41.8 to 16.7, and GLM 5.3 Flash costs 8.8x less per output token ($0.50 vs $4.40 per million). Updated October 22, 2026; 62 benchmarks have a result for both.
GLM 5.3 Flash or o4 Mini: which to pick
Only GLM 5.3 Flash has a coding score, 57.7 on the Vibe Coding Index; o4 Mini has none, so the two cannot be ranked on coding.
Pick o4 Mini for work measured by LiveCodeBench (Vals AI) or Chess puzzles, where it leads.
- Shared results
- 62
- GLM 5.3 Flash only
- 40
- o4 Mini only
- 36
- 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 5.3 Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 41.8 against 16.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 Flash has a AA Agentic Index result, 50.9.
Directional only
Reasoning
GPQA Diamond
GLM 5.3 Flash
GLM 5.3 Flash leads, 86.4% against 74.5%.
Same board, both measured
Math
AIME
No call
Only o4 Mini has a AIME result, 83.7%.
Directional only
Knowledge
MMLU Pro
GLM 5.3 Flash
GLM 5.3 Flash leads, 86.1% against 80.6%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3 Flash
GLM 5.3 Flash leads, 1272 against 985.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 Flash has a Vals Index result, 50.9%.
Directional only
Long documents
Listed context window
GLM 5.3 Flash
GLM 5.3 Flash takes 1.05M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 Flash
GLM 5.3 Flash $0.0004, o4 Mini $0.0033 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 Flash has the number, $0.02.
Directional only
At a glance
- Coding index
- GLM 5.3 Flash 57.7 from 6 benchmarks, o4 Mini not benchmarked.
- Widest category
- Web design, 40.3 points apart on average over 6 shared benchmarks.
- Widest single gap
- ARC-AGI-2: GLM 5.3 Flash 65.8% against o4 Mini 6.1%, 59.7 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 Flash $0.50 against o4 Mini $4.40 per million output tokens, 8.8x apart.
- Context window
- GLM 5.3 Flash takes 1.05M 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 5.3 Flash | Bars | o4 Mini | Ties |
|---|---|---|---|---|
| Coding | 2 | 1 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 13 | 1 | ||
| Math | 5 | 0 | ||
| Knowledge | 7 | 0 | ||
| Web design | 6 | 0 | ||
| General | 25 | 0 | ||
| All shared boards | 60 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index21
| Benchmark | GLM 5.3 Flash | 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 |
| LiveBench coding | 80.6% | Not measured | Only GLM 5.3 Flash measured |
| LMArena WebDev | 1607 | Not measured | Only GLM 5.3 Flash measured |
| AA Coding Index | 71.5 | Not measured | Only GLM 5.3 Flash measured |
| LiveCodeBench (Vals AI) | 80.5% | 82.2% | o4 Mini |
| SWE-bench Verified (Vals AI) | 92.0% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench 4 (Vals AI) | 37.4% | Not measured | Only GLM 5.3 Flash measured |
| Vibe Code Bench (Vals AI) | 30.8% | Not measured | Only GLM 5.3 Flash measured |
| Code migration (Vals AI) | 20.5% | Not measured | Only GLM 5.3 Flash measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only GLM 5.3 Flash measured |
| IOI (Vals AI) | 52.5% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 62.9% | Not measured | Only GLM 5.3 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | 16.0% | Not measured | Only GLM 5.3 Flash measured |
| SkillsBench (Vals AI) | 40.2% | Not measured | Only GLM 5.3 Flash measured |
| CyberBench (Vals AI) | 67.9% | Not measured | Only GLM 5.3 Flash measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 85.9% | Only o4 Mini measured |
| SciCode | 51.6% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 15.2% | Only o4 Mini measured |
| FrontierCode | 31.8% | Not measured | Only GLM 5.3 Flash measured |
| FrontierSWE | 18.1% | Not measured | Only GLM 5.3 Flash measured |
| GSO-Bench | Not measured | 3.6% | Only o4 Mini measured |
| WeirdML | Not measured | 52.6% | Only o4 Mini measured |
| ALE-Bench | 303.6 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: coding | 1507 | 1368 | GLM 5.3 Flash |
| LMArena Text: software and IT | 1500 | 1367 | GLM 5.3 Flash |
| LMArena WebDev: React | 1624 | Not measured | Only GLM 5.3 Flash measured |
| LMArena WebDev: HTML | 1577 | Not measured | Only GLM 5.3 Flash measured |
| LMArena WebDev: from an image | 1578 | Not measured | Only GLM 5.3 Flash measured |
| SciCode | Not measured | 10.8% | Only o4 Mini measured |
| Neither model has a result on the other 8 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 Flash | o4 Mini | Leader |
|---|---|---|---|
| AA Intelligence Index | 41.8 | 16.7 | GLM 5.3 Flash |
| Epoch Capabilities Index | 153.1 | 145.5 | GLM 5.3 Flash |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | GLM 5.3 Flash | o4 Mini | Leader |
|---|---|---|---|
| AA Agentic Index | 50.9 | Not measured | Only GLM 5.3 Flash measured |
| LiveBench research | 70.1% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench browser | 84.0% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench long agentic | 28.4% | Not measured | Only GLM 5.3 Flash measured |
| SREBench (Vals AI) | 0.8% | Not measured | Only GLM 5.3 Flash measured |
| Finance Agent (Vals AI) | 57.9% | Not measured | Only GLM 5.3 Flash measured |
| Tax Agent Bench (Vals AI) | 31.6% | Not measured | Only GLM 5.3 Flash measured |
| Legal Research Bench (Vals AI) | 45.2% | Not measured | Only GLM 5.3 Flash measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.4% | Not measured | Only GLM 5.3 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 55.9% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench-Science (Vals AI) | 2.9% | Not measured | Only GLM 5.3 Flash measured |
| τ²-Bench Telecom | Not measured | 55.6% | Only o4 Mini measured |
| APEX-Agents | 52.8% | Not measured | Only GLM 5.3 Flash measured |
| METR time horizons | Not measured | 63.9% | Only o4 Mini measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 53.2% | Only o4 Mini measured |
| Neither model has a result on the other 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index131
| Benchmark | GLM 5.3 Flash | o4 Mini | Leader |
|---|---|---|---|
| GPQA Diamond | 86.4% | 74.5% | GLM 5.3 Flash |
| LiveBench reasoning | 77.6% | Not measured | Only GLM 5.3 Flash measured |
| MMMU Pro (Vals AI) | 86.0% | 79.7% | GLM 5.3 Flash |
| GPQA Diamond (Artificial Analysis run) | 91.2% | 78.4% | GLM 5.3 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 39.9% | 16.5% | GLM 5.3 Flash |
| GPQA Diamond (Epoch AI run) | 90.2% | 79.6% | GLM 5.3 Flash |
| ARC-AGI | 91.0% | 58.7% | GLM 5.3 Flash |
| ARC-AGI-2 | 65.8% | 6.1% | GLM 5.3 Flash |
| SimpleBench | Not measured | 38.7% | Only o4 Mini measured |
| Chess puzzles | 21.0% | 22.0% | o4 Mini |
| Mystery game puzzles | 8.0% | 5.0% | GLM 5.3 Flash |
| VPCT | Not measured | 57.5% | Only o4 Mini measured |
| LMArena Text: hard prompts | 1490 | 1351 | GLM 5.3 Flash |
| LMArena Text: expert | 1511 | 1343 | GLM 5.3 Flash |
| LMArena Text: hard prompts in English | 1488 | 1366 | GLM 5.3 Flash |
| LMArena Vision: diagrams | 1317 | 1197 | GLM 5.3 Flash |
| LMArena Vision: homework | 1318 | 1234 | GLM 5.3 Flash |
| Kagi LLM Benchmark | Not measured | 67.6% | Only o4 Mini measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5.3 Flash | o4 Mini | Leader |
|---|---|---|---|
| AIME | Not measured | 83.7% | Only o4 Mini measured |
| MATH 500 (Vals AI) | Not measured | 94.2% | Only o4 Mini measured |
| MGSM (Vals AI) | Not measured | 93.4% | Only o4 Mini measured |
| AA Math Index | Not measured | 90.7 | Only o4 Mini measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 90.7% | Only o4 Mini measured |
| OTIS Mock AIME | 93.9% | 81.7% | GLM 5.3 Flash |
| FrontierMath, tiers 1 to 3 | 55.8% | 36.1% | GLM 5.3 Flash |
| FrontierMath, tier 4 | 17.1% | 4.9% | GLM 5.3 Flash |
| ProofBench | 21.0% | Not measured | Only GLM 5.3 Flash measured |
| MATH level 5 | Not measured | 97.8% | Only o4 Mini measured |
| LMArena Text: math | 1503 | 1389 | GLM 5.3 Flash |
| LMArena Text: mathematical work | 1497 | 1379 | GLM 5.3 Flash |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GLM 5.3 Flash | o4 Mini | Leader |
|---|---|---|---|
| MMLU Pro | 86.1% | 80.6% | GLM 5.3 Flash |
| LegalBench (Vals AI) | 83.9% | 79.2% | GLM 5.3 Flash |
| CorpFin (Vals AI) | Not measured | 59.0% | Only o4 Mini measured |
| TaxEval (Vals AI) | 75.6% | 74.8% | GLM 5.3 Flash |
| MortgageTax (Vals AI) | Not measured | 64.8% | Only o4 Mini measured |
| MedQA (Vals AI) | Not measured | 96.0% | Only o4 Mini measured |
| MedCode (Vals AI) | Not measured | 33.8% | Only o4 Mini measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 83.2% | Only o4 Mini measured |
| SimpleQA Verified | Not measured | 19.6% | Only o4 Mini measured |
| GeoBench | Not measured | 64.0% | Only o4 Mini measured |
| LMArena Text: science | 1484 | 1354 | GLM 5.3 Flash |
| LMArena Text: medicine and healthcare | 1475 | 1361 | GLM 5.3 Flash |
| LMArena Text: legal and government | 1473 | 1350 | GLM 5.3 Flash |
| LMArena Text: business and finance | 1460 | 1325 | GLM 5.3 Flash |
| FACTS Benchmark Suite | Not measured | 28.1% | Only o4 Mini measured |
| 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 5.3 Flash | o4 Mini | Leader |
|---|---|---|---|
| Design Arena: websites | 1272 | 985 | GLM 5.3 Flash |
| Design Arena: UI components | 1316 | 983 | GLM 5.3 Flash |
| Design Arena: game dev | 1278 | 1003 | GLM 5.3 Flash |
| Design Arena: data viz | 1262 | 991 | GLM 5.3 Flash |
| Design Arena: 3D | 1316 | 854 | GLM 5.3 Flash |
| Design Arena: all code categories | 1282 | 976 | GLM 5.3 Flash |
| Design Arena: SVG | 1285 | Not measured | Only GLM 5.3 Flash measured |
| Design Arena: ASCII art | 1267 | Not measured | Only GLM 5.3 Flash measured |
| LiveBench design | 77.2% | Not measured | Only GLM 5.3 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index250
| Benchmark | GLM 5.3 Flash | o4 Mini | Leader |
|---|---|---|---|
| Vals Index | 50.9% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench data analysis | 76.4% | Not measured | Only GLM 5.3 Flash measured |
| SAGE (Vals AI) | Not measured | 41.1% | Only o4 Mini measured |
| MedScribe (Vals AI) | 88.9% | 69.1% | GLM 5.3 Flash |
| IFBench | Not measured | 68.7% | Only o4 Mini measured |
| Long context reasoning (AA-LCR) | 80.0% | 61.0% | GLM 5.3 Flash |
| 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 |
| Surface Evolver Bench | 52.5% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text | 1471 | 1353 | GLM 5.3 Flash |
| LMArena Text: instruction following | 1478 | 1320 | GLM 5.3 Flash |
| LMArena Text: creative writing | 1441 | 1294 | GLM 5.3 Flash |
| LMArena Vision | 1294 | 1194 | GLM 5.3 Flash |
| LMArena Text: English | 1474 | 1367 | GLM 5.3 Flash |
| LMArena Text: non-English | 1462 | 1337 | GLM 5.3 Flash |
| LMArena Text: Chinese | 1531 | 1354 | GLM 5.3 Flash |
| LMArena Text: German | 1462 | 1336 | GLM 5.3 Flash |
| LMArena Text: Japanese | 1444 | 1308 | GLM 5.3 Flash |
| LMArena Text: Korean | 1436 | 1312 | GLM 5.3 Flash |
| LMArena Text: French | 1491 | 1364 | GLM 5.3 Flash |
| LMArena Text: Spanish | 1475 | 1346 | GLM 5.3 Flash |
| LMArena Text: Russian | 1465 | 1334 | GLM 5.3 Flash |
| LMArena Text: Polish | 1450 | 1358 | GLM 5.3 Flash |
| LMArena Text: multi-turn | 1469 | 1350 | GLM 5.3 Flash |
| LMArena Text: longer queries | 1481 | 1315 | GLM 5.3 Flash |
| LMArena Text: writing and language | 1455 | 1316 | GLM 5.3 Flash |
| LMArena Text: entertainment and media | 1440 | 1308 | GLM 5.3 Flash |
| LMArena Vision: English | 1295 | 1200 | GLM 5.3 Flash |
| LMArena Vision: Chinese | 1363 | 1218 | GLM 5.3 Flash |
| LMArena Vision: reading text | 1299 | 1197 | GLM 5.3 Flash |
| LMArena Vision: humor | 1279 | 1199 | GLM 5.3 Flash |
| 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 | 1270 | 1166 | GLM 5.3 Flash |
| Neither model has a result on the other 10 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 Flash | 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 5.3 Flash costs less
Repository review
50K input + 3K output tokens
GLM 5.3 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 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 5.3 Flash | OpenAI o4 Mini |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 26, 2026 | April 16, 2025 |
| Context window | 1.05M | 200K |
| Input $/Mtok | $0.15 | $1.10 |
| Output $/Mtok | $0.50 | $4.40 |
| Coding index | 57.7 | Not benchmarked |
| Independent measurements behind it | 6 | 0 |
| Cost per solved task | $0.02 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 Flash better than o4 Mini for coding?
Only GLM 5.3 Flash has a coding score, 57.7 on the Vibe Coding Index; o4 Mini has none, so the two cannot be ranked on coding. Pick o4 Mini for work measured by LiveCodeBench (Vals AI) or Chess puzzles, where it leads.
Which is cheaper, GLM 5.3 Flash or o4 Mini?
GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; o4 Mini costs $1.10 and $4.40. Cost per solved task is known for GLM 5.3 Flash only.
Which has the bigger context window, GLM 5.3 Flash or o4 Mini?
GLM 5.3 Flash, with 1.05M tokens against 200K.
How many benchmarks is the GLM 5.3 Flash vs o4 Mini comparison based on?
62 benchmarks have a result for both models as of October 22, 2026. GLM 5.3 Flash's coding index rests on 6 independent benchmarks and o4 Mini's on 0. Where only one model has a result, the page says so instead of naming a winner.