GLM 4.5 vs GPT-5 Mini
GPT-5 Mini leads GLM 4.5 on the Artificial Analysis Intelligence Index, 20.6 to 12.8, at about the same output price ($2.00 vs $2.20 per million). Updated October 22, 2026; 60 benchmarks have a result for both.
GLM 4.5 or GPT-5 Mini: which to pick
Only GPT-5 Mini has a coding score, 34.2 on the Vibe Coding Index; GLM 4.5 has none, so the two cannot be ranked on coding.
Pick GLM 4.5 for work measured by SWE-bench Verified or LMArena Text: coding, where it leads.
- Shared results
- 60
- GLM 4.5 only
- 1
- GPT-5 Mini only
- 41
- 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 GPT-5 Mini has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5 Mini
GPT-5 Mini leads, 20.6 against 12.8.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5 Mini
GPT-5 Mini leads, 80.3% against 72.2%.
Same board, both measured
Math
AIME
GPT-5 Mini
GPT-5 Mini leads, 91.5% against 86.7%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5 Mini
GPT-5 Mini leads, 82.2% against 81.2%.
Same board, both measured
Web design
Design Arena: websites
GLM 4.5
GLM 4.5 leads, 1169 against 1125.
Same board, both measured
Long documents
Listed context window
GPT-5 Mini
GPT-5 Mini takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Mini
GLM 4.5 $0.0017, GPT-5 Mini $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5 Mini has the number, $0.08.
Directional only
At a glance
- Coding index
- GLM 4.5 not benchmarked, GPT-5 Mini 34.2 from 2 benchmarks.
- Widest category
- Agentic work, 28.1 points apart on average over 1 shared benchmark.
- Widest single gap
- IFBench: GLM 4.5 44.1% against GPT-5 Mini 75.4%, 31.4 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 $2.20 against GPT-5 Mini $2.00 per million output tokens, 1.1x apart.
- Context window
- GLM 4.5 takes 131K tokens, GPT-5 Mini 400K.
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 | GPT-5 Mini | Ties |
|---|---|---|---|---|
| Coding | 3 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 3 | 4 | ||
| Math | 2 | 5 | ||
| Knowledge | 5 | 5 | ||
| Web design | 7 | 0 | ||
| General | 17 | 3 | ||
| All shared boards | 37 | 23 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index34
| Benchmark | GLM 4.5 | GPT-5 Mini | Leader |
|---|---|---|---|
| SWE-bench Verified | 64.2% | 59.8% | GLM 4.5 |
| SWE-bench Multilingual | Not measured | 39.7% | Only GPT-5 Mini measured |
| LiveCodeBench (Vals AI) | 67.5% | 86.6% | GPT-5 Mini |
| SWE-bench Verified (Vals AI) | Not measured | 60.8% | Only GPT-5 Mini measured |
| Vibe Code Bench (Vals AI) | Not measured | 14.2% | Only GPT-5 Mini measured |
| LiveCodeBench (Artificial Analysis run) | 73.8% | 83.8% | GPT-5 Mini |
| SciCode | Not measured | 39.0% | Only GPT-5 Mini measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | 33.3% | GPT-5 Mini |
| Terminal-Bench | Not measured | 34.8% | Only GPT-5 Mini measured |
| WeirdML | 40.6% | 52.7% | GPT-5 Mini |
| ALE-Bench | 344.8 | Not measured | Only GLM 4.5 measured |
| LMArena Text: coding | 1434 | 1406 | GLM 4.5 |
| LMArena Text: software and IT | 1440 | 1394 | GLM 4.5 |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | GPT-5 Mini | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.8 | 20.6 | GPT-5 Mini |
| Epoch Capabilities Index | Not measured | 145.3 | Only GPT-5 Mini measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | GPT-5 Mini | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 43.0% | 71.1% | GPT-5 Mini |
| Vending-Bench 2 | Not measured | -$31 | Only GPT-5 Mini measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 55.5% | Only GPT-5 Mini measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index34
| Benchmark | GLM 4.5 | GPT-5 Mini | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | 80.3% | GPT-5 Mini |
| MMMU Pro (Vals AI) | Not measured | 78.9% | Only GPT-5 Mini measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | 82.8% | GPT-5 Mini |
| Humanity's Last Exam (Artificial Analysis run) | 13.0% | 21.5% | GPT-5 Mini |
| GPQA Diamond (Epoch AI run) | Not measured | 75.0% | Only GPT-5 Mini measured |
| ARC-AGI | Not measured | 54.3% | Only GPT-5 Mini measured |
| ARC-AGI-2 | Not measured | 4.4% | Only GPT-5 Mini measured |
| Chess puzzles | Not measured | 24.0% | Only GPT-5 Mini measured |
| Mystery game puzzles | Not measured | 10.0% | Only GPT-5 Mini measured |
| VPCT | Not measured | 40.2% | Only GPT-5 Mini measured |
| LMArena Text: hard prompts | 1429 | 1380 | GLM 4.5 |
| LMArena Text: expert | 1433 | 1379 | GLM 4.5 |
| LMArena Text: hard prompts in English | 1429 | 1383 | GLM 4.5 |
| LMArena Vision: diagrams | Not measured | 1202 | Only GPT-5 Mini measured |
| LMArena Vision: homework | Not measured | 1210 | Only GPT-5 Mini measured |
| Kagi LLM Benchmark | 57.9% | 70.3% | GPT-5 Mini |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index25
| Benchmark | GLM 4.5 | GPT-5 Mini | Leader |
|---|---|---|---|
| AIME | 86.7% | 91.5% | GPT-5 Mini |
| MATH 500 (Vals AI) | 94.0% | 94.8% | GPT-5 Mini |
| MGSM (Vals AI) | 90.8% | 92.6% | GPT-5 Mini |
| AA Math Index | 73.7 | 90.7 | GPT-5 Mini |
| AIME 2025 (Artificial Analysis run) | 73.7% | 90.7% | GPT-5 Mini |
| OTIS Mock AIME | Not measured | 86.7% | Only GPT-5 Mini measured |
| FrontierMath, tiers 1 to 3 | Not measured | 46.7% | Only GPT-5 Mini measured |
| FrontierMath, tier 4 | Not measured | 12.2% | Only GPT-5 Mini measured |
| ProofBench | Not measured | 9.0% | Only GPT-5 Mini measured |
| MATH level 5 | Not measured | 97.8% | Only GPT-5 Mini measured |
| LMArena Text: math | 1427 | 1378 | GLM 4.5 |
| LMArena Text: mathematical work | 1430 | 1380 | GLM 4.5 |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index55
| Benchmark | GLM 4.5 | GPT-5 Mini | Leader |
|---|---|---|---|
| MMLU Pro | 81.2% | 82.2% | GPT-5 Mini |
| LegalBench (Vals AI) | 75.6% | 81.8% | GPT-5 Mini |
| CaseLaw (Vals AI) | Not measured | 68.5% | Only GPT-5 Mini measured |
| CorpFin (Vals AI) | 61.0% | 60.2% | GLM 4.5 |
| TaxEval (Vals AI) | 72.4% | 75.2% | GPT-5 Mini |
| MortgageTax (Vals AI) | Not measured | 66.9% | Only GPT-5 Mini measured |
| MedQA (Vals AI) | 90.0% | 96.1% | GPT-5 Mini |
| MedCode (Vals AI) | Not measured | 43.0% | Only GPT-5 Mini measured |
| MMLU Pro (Artificial Analysis run) | 83.5% | 83.7% | GPT-5 Mini |
| SimpleQA Verified | Not measured | 21.6% | Only GPT-5 Mini measured |
| LMArena Text: science | 1444 | 1371 | GLM 4.5 |
| LMArena Text: medicine and healthcare | 1455 | 1380 | GLM 4.5 |
| LMArena Text: legal and government | 1438 | 1375 | GLM 4.5 |
| LMArena Text: business and finance | 1420 | 1358 | GLM 4.5 |
| FACTS Benchmark Suite | Not measured | 38.6% | Only GPT-5 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 index70
| Benchmark | GLM 4.5 | GPT-5 Mini | Leader |
|---|---|---|---|
| Design Arena: websites | 1169 | 1125 | GLM 4.5 |
| Design Arena: UI components | 1141 | 1103 | GLM 4.5 |
| Design Arena: game dev | 1144 | 1129 | GLM 4.5 |
| Design Arena: data viz | 1159 | 1131 | GLM 4.5 |
| Design Arena: 3D | 1170 | 1036 | GLM 4.5 |
| Design Arena: all code categories | 1165 | 1114 | GLM 4.5 |
| Design Arena: SVG | 1096 | 1091 | GLM 4.5 |
| Design Arena: ASCII art | Not measured | 1125 | Only GPT-5 Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index173
| Benchmark | GLM 4.5 | GPT-5 Mini | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 43.0% | Only GPT-5 Mini measured |
| MedScribe (Vals AI) | Not measured | 80.6% | Only GPT-5 Mini measured |
| IFBench | 44.1% | 75.4% | GPT-5 Mini |
| Long context reasoning (AA-LCR) | 52.7% | 72.3% | GPT-5 Mini |
| DTBench | Not measured | 80.5% | Only GPT-5 Mini measured |
| LMCA | Not measured | 34.2% | Only GPT-5 Mini measured |
| Fiction.LiveBench | Not measured | 69.4% | Only GPT-5 Mini measured |
| Creative writing (Lech Mazur) | 73.4% | 83.1% | GPT-5 Mini |
| LMArena Text | 1430 | 1373 | GLM 4.5 |
| LMArena Text: instruction following | 1404 | 1357 | GLM 4.5 |
| LMArena Text: creative writing | 1395 | 1325 | GLM 4.5 |
| LMArena Vision | Not measured | 1202 | Only GPT-5 Mini measured |
| LMArena Text: English | 1437 | 1379 | GLM 4.5 |
| LMArena Text: non-English | 1417 | 1363 | GLM 4.5 |
| LMArena Text: Chinese | 1465 | 1386 | GLM 4.5 |
| LMArena Text: German | 1407 | 1366 | GLM 4.5 |
| LMArena Text: Japanese | 1415 | 1341 | GLM 4.5 |
| LMArena Text: Korean | 1380 | 1308 | GLM 4.5 |
| LMArena Text: French | 1418 | 1386 | GLM 4.5 |
| LMArena Text: Spanish | 1454 | 1354 | GLM 4.5 |
| LMArena Text: Russian | 1413 | 1362 | GLM 4.5 |
| LMArena Text: Polish | 1422 | 1378 | GLM 4.5 |
| LMArena Text: multi-turn | 1415 | 1363 | GLM 4.5 |
| LMArena Text: longer queries | 1412 | 1355 | GLM 4.5 |
| LMArena Text: writing and language | 1403 | 1344 | GLM 4.5 |
| LMArena Text: entertainment and media | 1404 | 1340 | GLM 4.5 |
| LMArena Vision: English | Not measured | 1216 | Only GPT-5 Mini measured |
| LMArena Vision: Chinese | Not measured | 1214 | Only GPT-5 Mini measured |
| LMArena Vision: reading text | Not measured | 1199 | Only GPT-5 Mini measured |
| LMArena Vision: humor | Not measured | 1205 | Only GPT-5 Mini measured |
| LMArena Vision: captioning | Not measured | 1217 | Only GPT-5 Mini measured |
| LMArena Vision: recognizing things | Not measured | 1226 | Only GPT-5 Mini measured |
| LMArena Vision: creative writing | Not measured | 1181 | Only GPT-5 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 | GPT-5 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
GPT-5 Mini costs less
Repository review
50K input + 3K output tokens
GPT-5 Mini costs less
Agent loop
200K input + 10K output tokens
GPT-5 Mini 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 GPT-5 Mini |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | July 25, 2025 | August 7, 2025 |
| Context window | 131K | 400K |
| Input $/Mtok | $0.60 | $0.25 |
| Output $/Mtok | $2.20 | $2.00 |
| Coding index | Not benchmarked | 34.2 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $0.08 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5 better than GPT-5 Mini for coding?
Only GPT-5 Mini has a coding score, 34.2 on the Vibe Coding Index; GLM 4.5 has none, so the two cannot be ranked on coding. Pick GLM 4.5 for work measured by SWE-bench Verified or LMArena Text: coding, where it leads.
Which is cheaper, GLM 4.5 or GPT-5 Mini?
GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; GPT-5 Mini costs $0.25 and $2.00. Cost per solved task is known for GPT-5 Mini only.
Which has the bigger context window, GLM 4.5 or GPT-5 Mini?
GPT-5 Mini, with 400K tokens against 131K.
How many benchmarks is the GLM 4.5 vs GPT-5 Mini comparison based on?
60 benchmarks have a result for both models as of October 22, 2026. GLM 4.5's coding index rests on 0 independent benchmarks and GPT-5 Mini's on 2. Where only one model has a result, the page says so instead of naming a winner.