GLM 5.3 vs MiMo-V2.6-Flash
GLM 5.3 leads MiMo-V2.6-Flash on coding, 70.6 to 54.7 on the Vibe Coding Index, and MiMo-V2.6-Flash costs 15.7x less per output token ($0.28 vs $4.40 per million). Updated October 22, 2026; 56 benchmarks have a result for both.
GLM 5.3 or MiMo-V2.6-Flash: which to pick
Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 54.7 for MiMo-V2.6-Flash.
Pick MiMo-V2.6-Flash for output tokens 15.7x cheaper ($0.28 vs $4.40 per million) and a larger context window (1.05M against 1.05M tokens).
- Shared results
- 56
- GLM 5.3 only
- 44
- MiMo-V2.6-Flash only
- 10
- 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
GLM 5.3
GLM 5.3 scores 70.6 against 54.7.
6 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 37.9.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 has a AA Agentic Index result, 53.1.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.3 has a GPQA Diamond result, 88.1%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 has a MMLU Pro result, 86.8%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 5.3 has a Design Arena: websites result, 1295.
Directional only
General
Vals Index
GLM 5.3
GLM 5.3 leads, 59.7% against 54.8%.
Same board, both measured
Long documents
Listed context window
MiMo-V2.6-Flash
MiMo-V2.6-Flash takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiMo-V2.6-Flash
GLM 5.3 $0.0036, MiMo-V2.6-Flash $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 has the number, $0.36.
Directional only
At a glance
- Coding index
- GLM 5.3 70.6 from 6 benchmarks, MiMo-V2.6-Flash 54.7 from 3 benchmarks.
- Widest category
- Intelligence, 6.9 points apart on average over 1 shared benchmark.
- Widest single gap
- IOI (Vals AI): GLM 5.3 68.4% against MiMo-V2.6-Flash 47.7%, 20.8 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $4.40 against MiMo-V2.6-Flash $0.28 per million output tokens, 15.7x apart.
- Context window
- GLM 5.3 takes 1.05M tokens, MiMo-V2.6-Flash 1.05M.
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 | Bars | MiMo-V2.6-Flash | Ties |
|---|---|---|---|---|
| Coding | 6 | 8 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 5 | 2 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 1 | ||
| Knowledge | 5 | 0 | ||
| General | 21 | 0 | ||
| All shared boards | 45 | 11 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index68
| Benchmark | GLM 5.3 | MiMo-V2.6-Flash | Leader |
|---|---|---|---|
| LiveBench coding | 82.8% | Not measured | Only GLM 5.3 measured |
| LMArena WebDev | 1621 | 1634 | MiMo-V2.6-Flash |
| AA Coding Index | 74.8 | Not measured | Only GLM 5.3 measured |
| LiveCodeBench (Vals AI) | 80.5% | Not measured | Only GLM 5.3 measured |
| SWE-bench Verified (Vals AI) | 95.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 4 (Vals AI) | 38.9% | 24.2% | GLM 5.3 |
| Vibe Code Bench (Vals AI) | 78.1% | 79.0% | MiMo-V2.6-Flash |
| Code migration (Vals AI) | 44.2% | 40.9% | GLM 5.3 |
| ProgramBench (Vals AI) | 1.5% | 0.5% | GLM 5.3 |
| IOI (Vals AI) | 68.4% | 47.7% | GLM 5.3 |
| Terminal-Bench 2.1 (Vals AI) | 71.5% | 76.4% | MiMo-V2.6-Flash |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GLM 5.3 measured |
| SkillsBench (Vals AI) | 47.5% | Not measured | Only GLM 5.3 measured |
| CyberBench (Vals AI) | 72.1% | 75.4% | MiMo-V2.6-Flash |
| SciCode | 59.0% | 51.3% | GLM 5.3 |
| FrontierCode | 40.1% | Not measured | Only GLM 5.3 measured |
| FrontierSWE | 30.2% | Not measured | Only GLM 5.3 measured |
| WeirdML | 75.4% | Not measured | Only GLM 5.3 measured |
| ALE-Bench | 1317.4 | 1244.2 | GLM 5.3 |
| LMArena Text: coding | 1497 | 1499 | MiMo-V2.6-Flash |
| LMArena Text: software and IT | 1493 | 1497 | MiMo-V2.6-Flash |
| LMArena WebDev: React | 1641 | 1648 | MiMo-V2.6-Flash |
| LMArena WebDev: HTML | 1595 | 1641 | MiMo-V2.6-Flash |
| LMArena WebDev: from an image | Not measured | 1596 | Only MiMo-V2.6-Flash measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.3 | MiMo-V2.6-Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | 37.9 | GLM 5.3 |
| Epoch Capabilities Index | 156.0 | Not measured | Only GLM 5.3 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index52
| Benchmark | GLM 5.3 | MiMo-V2.6-Flash | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | Not measured | Only GLM 5.3 measured |
| Design Arena: agents building mobile apps | 1194 | Not measured | Only GLM 5.3 measured |
| Design Arena: HTML slides | 1189 | Not measured | Only GLM 5.3 measured |
| Design Arena: PowerPoint slides | 1230 | Not measured | Only GLM 5.3 measured |
| LiveBench research | 73.2% | Not measured | Only GLM 5.3 measured |
| LiveBench browser | 0.0% | Not measured | Only GLM 5.3 measured |
| LiveBench long agentic | 35.9% | Not measured | Only GLM 5.3 measured |
| SREBench (Vals AI) | 9.2% | 4.2% | GLM 5.3 |
| Finance Agent (Vals AI) | 55.8% | 56.3% | MiMo-V2.6-Flash |
| Tax Agent Bench (Vals AI) | 39.7% | 28.5% | GLM 5.3 |
| Legal Research Bench (Vals AI) | 49.0% | 38.0% | GLM 5.3 |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | 13.3% | GLM 5.3 |
| Excel Modeling Benchmark (Vals AI) | 56.3% | 65.5% | MiMo-V2.6-Flash |
| BioMysteryBench (Vals AI) | Not measured | 69.3% | Only MiMo-V2.6-Flash measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | 4.3% | GLM 5.3 |
| Vals Science Index | Not measured | 31.7% | Only MiMo-V2.6-Flash measured |
| APEX-Agents | 56.6% | Not measured | Only GLM 5.3 measured |
| Vending-Bench 2 | $8,164 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5.3 | MiMo-V2.6-Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | Not measured | Only GLM 5.3 measured |
| LiveBench reasoning | 85.8% | Not measured | Only GLM 5.3 measured |
| MysteryMechanism (Vals AI) | 23.0% | 21.6% | GLM 5.3 |
| GPQA Diamond (Artificial Analysis run) | 91.7% | Not measured | Only GLM 5.3 measured |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 35.1% | GLM 5.3 |
| GPQA Diamond (Epoch AI run) | 90.9% | Not measured | Only GLM 5.3 measured |
| Chess puzzles | 37.0% | Not measured | Only GLM 5.3 measured |
| Mystery game puzzles | 33.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: hard prompts | 1490 | 1475 | GLM 5.3 |
| LMArena Text: expert | 1514 | 1496 | GLM 5.3 |
| LMArena Text: hard prompts in English | 1496 | 1493 | GLM 5.3 |
| LMArena Vision: diagrams | Not measured | 1276 | Only MiMo-V2.6-Flash measured |
| LMArena Vision: homework | Not measured | 1283 | Only MiMo-V2.6-Flash measured |
| Neither model has a result on the other 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | GLM 5.3 | MiMo-V2.6-Flash | Leader |
|---|---|---|---|
| OTIS Mock AIME | 91.1% | Not measured | Only GLM 5.3 measured |
| FrontierMath, tiers 1 to 3 | 68.8% | Not measured | Only GLM 5.3 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.3 measured |
| ProofBench | 49.0% | 63.0% | MiMo-V2.6-Flash |
| LMArena Text: math | 1495 | 1476 | GLM 5.3 |
| LMArena Text: mathematical work | 1497 | 1478 | GLM 5.3 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5.3 | MiMo-V2.6-Flash | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | Not measured | Only GLM 5.3 measured |
| LegalBench (Vals AI) | 84.8% | Not measured | Only GLM 5.3 measured |
| TaxEval (Vals AI) | 72.4% | Not measured | Only GLM 5.3 measured |
| MedCode (Vals AI) | 42.9% | 41.1% | GLM 5.3 |
| SimpleQA Verified | 41.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: science | 1498 | 1462 | GLM 5.3 |
| LMArena Text: medicine and healthcare | 1481 | 1439 | GLM 5.3 |
| LMArena Text: legal and government | 1473 | 1441 | GLM 5.3 |
| LMArena Text: business and finance | 1459 | 1454 | GLM 5.3 |
| Neither model has a result on the other 9 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GLM 5.3 | MiMo-V2.6-Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | Not measured | Only GLM 5.3 measured |
| Design Arena: UI components | 1314 | Not measured | Only GLM 5.3 measured |
| Design Arena: game dev | 1311 | Not measured | Only GLM 5.3 measured |
| Design Arena: data viz | 1260 | Not measured | Only GLM 5.3 measured |
| Design Arena: 3D | 1332 | Not measured | Only GLM 5.3 measured |
| Design Arena: all code categories | 1304 | Not measured | Only GLM 5.3 measured |
| Design Arena: SVG | 1294 | Not measured | Only GLM 5.3 measured |
| Design Arena: ASCII art | 1208 | Not measured | Only GLM 5.3 measured |
| LiveBench design | 77.3% | Not measured | Only GLM 5.3 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index210
| Benchmark | GLM 5.3 | MiMo-V2.6-Flash | Leader |
|---|---|---|---|
| Vals Index | 59.7% | 54.8% | GLM 5.3 |
| LiveBench data analysis | 70.2% | Not measured | Only GLM 5.3 measured |
| Public Benefits Bench (Vals AI) | 68.5% | 67.6% | GLM 5.3 |
| SAGE (Vals AI) | Not measured | 43.5% | Only MiMo-V2.6-Flash measured |
| MedScribe (Vals AI) | 88.8% | 85.3% | GLM 5.3 |
| Long context reasoning (AA-LCR) | 79.7% | 74.3% | GLM 5.3 |
| DTBench | 87.7% | Not measured | Only GLM 5.3 measured |
| LMCA | 55.5% | Not measured | Only GLM 5.3 measured |
| LMArena Text | 1474 | 1453 | GLM 5.3 |
| LMArena Text: instruction following | 1479 | 1452 | GLM 5.3 |
| LMArena Text: creative writing | 1458 | 1403 | GLM 5.3 |
| LMArena Vision | Not measured | 1256 | Only MiMo-V2.6-Flash measured |
| LMArena Text: English | 1482 | 1476 | GLM 5.3 |
| LMArena Text: non-English | 1459 | 1431 | GLM 5.3 |
| LMArena Text: Chinese | 1532 | 1509 | GLM 5.3 |
| LMArena Text: German | 1488 | 1458 | GLM 5.3 |
| LMArena Text: Japanese | 1460 | 1359 | GLM 5.3 |
| LMArena Text: Korean | 1460 | 1425 | GLM 5.3 |
| LMArena Text: French | 1503 | 1480 | GLM 5.3 |
| LMArena Text: Spanish | 1462 | 1453 | GLM 5.3 |
| LMArena Text: Russian | 1469 | 1409 | GLM 5.3 |
| LMArena Text: Polish | 1466 | 1455 | GLM 5.3 |
| LMArena Text: multi-turn | 1472 | 1447 | GLM 5.3 |
| LMArena Text: longer queries | 1483 | 1459 | GLM 5.3 |
| LMArena Text: writing and language | 1469 | 1424 | GLM 5.3 |
| LMArena Text: entertainment and media | 1447 | 1415 | GLM 5.3 |
| LMArena Vision: English | Not measured | 1269 | Only MiMo-V2.6-Flash measured |
| LMArena Vision: reading text | Not measured | 1270 | Only MiMo-V2.6-Flash measured |
| LMArena Vision: creative writing | Not measured | 1231 | Only MiMo-V2.6-Flash measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | MiMo-V2.6-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
MiMo-V2.6-Flash costs less
Repository review
50K input + 3K output tokens
MiMo-V2.6-Flash costs less
Agent loop
200K input + 10K output tokens
MiMo-V2.6-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 | Xiaomi MiMo-V2.6-Flash |
|---|---|---|
| Maker | Z.ai | Xiaomi |
| Weights | Open weights | Open weights |
| Released | August 18, 2026 | September 21, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $1.40 | $0.14 |
| Output $/Mtok | $4.40 | $0.28 |
| Coding index | 70.6 | 54.7 |
| Independent measurements behind it | 6 | 3 |
| Cost per solved task | $0.36 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than MiMo-V2.6-Flash for coding?
Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 54.7 for MiMo-V2.6-Flash. Pick MiMo-V2.6-Flash for output tokens 15.7x cheaper ($0.28 vs $4.40 per million) and a larger context window (1.05M against 1.05M tokens).
Which is cheaper, GLM 5.3 or MiMo-V2.6-Flash?
GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; MiMo-V2.6-Flash costs $0.14 and $0.28. Cost per solved task is known for GLM 5.3 only.
Which has the bigger context window, GLM 5.3 or MiMo-V2.6-Flash?
MiMo-V2.6-Flash, with 1.05M tokens against 1.05M.
How many benchmarks is the GLM 5.3 vs MiMo-V2.6-Flash comparison based on?
56 benchmarks have a result for both models as of October 22, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and MiMo-V2.6-Flash's on 3. Where only one model has a result, the page says so instead of naming a winner.