GLM 4.5 Air vs Mercury 2
GLM 4.5 Air leads Mercury 2 on the Artificial Analysis Intelligence Index, 11.1 to 10.8, and Mercury 2 costs 1.5x less per output token ($0.75 vs $1.10 per million). Updated October 22, 2026; 33 benchmarks have a result for both.
GLM 4.5 Air or Mercury 2: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.5 Air for a larger context window (131K against 128K tokens) and work measured by Terminal-Bench Hard (Artificial Analysis run) or τ²-Bench Telecom, where it leads.
- Shared results
- 33
- GLM 4.5 Air only
- 15
- Mercury 2 only
- 9
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.5 Air
GLM 4.5 Air leads, 11.1 against 10.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mercury 2 has a AA Agentic Index result, 1.6.
Directional only
Web design
Design Arena: websites
GLM 4.5 Air
GLM 4.5 Air leads, 1146 against 1036.
Same board, both measured
Long documents
Listed context window
GLM 4.5 Air
GLM 4.5 Air takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2
GLM 4.5 Air $0.0008, Mercury 2 $0.0006 per turn.
List prices
At a glance
- Coding index
- GLM 4.5 Air not benchmarked, Mercury 2 not benchmarked.
- Widest category
- Agentic work, 24.3 points apart on average over 1 shared benchmark.
- Widest single gap
- IFBench: GLM 4.5 Air 37.6% against Mercury 2 69.8%, 32.2 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 Air $1.10 against Mercury 2 $0.75 per million output tokens, 1.5x apart.
- Context window
- GLM 4.5 Air takes 131K tokens, Mercury 2 128K.
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 Air | Bars | Mercury 2 | Ties |
|---|---|---|---|---|
| Coding | 2 | 1 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 3 | 2 | ||
| Knowledge | 3 | 0 | ||
| Web design | 7 | 0 | ||
| General | 12 | 1 | ||
| All shared boards | 28 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index21
| Benchmark | GLM 4.5 Air | Mercury 2 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1171 | Only Mercury 2 measured |
| AA Coding Index | Not measured | 31.1 | Only Mercury 2 measured |
| LiveCodeBench (Artificial Analysis run) | 68.4% | Not measured | Only GLM 4.5 Air measured |
| SciCode | Not measured | 37.7% | Only Mercury 2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 20.5% | 26.5% | Mercury 2 |
| GSO-Bench | 2.9% | Not measured | Only GLM 4.5 Air measured |
| WeirdML | Not measured | 43.2% | Only Mercury 2 measured |
| ALE-Bench | Not measured | 785.6 | Only Mercury 2 measured |
| LMArena Text: coding | 1397 | 1390 | GLM 4.5 Air |
| LMArena Text: software and IT | 1401 | 1385 | GLM 4.5 Air |
| LMArena WebDev: React | Not measured | 1160 | Only Mercury 2 measured |
| LMArena WebDev: HTML | Not measured | 1199 | Only Mercury 2 measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.5 Air | Mercury 2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.1 | 10.8 | GLM 4.5 Air |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 Air | Mercury 2 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.6 | Only Mercury 2 measured |
| τ²-Bench Telecom | 46.5% | 70.8% | Mercury 2 |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | GLM 4.5 Air | Mercury 2 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 73.3% | 77.0% | Mercury 2 |
| Humanity's Last Exam (Artificial Analysis run) | 7.0% | 17.1% | Mercury 2 |
| LMArena Text: hard prompts | 1379 | 1362 | GLM 4.5 Air |
| LMArena Text: expert | 1370 | 1358 | GLM 4.5 Air |
| LMArena Text: hard prompts in English | 1388 | 1374 | GLM 4.5 Air |
| Kagi LLM Benchmark | 43.0% | Not measured | Only GLM 4.5 Air measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GLM 4.5 Air | Mercury 2 | Leader |
|---|---|---|---|
| AA Math Index | 80.7 | Not measured | Only GLM 4.5 Air measured |
| AIME 2025 (Artificial Analysis run) | 80.7% | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: math | 1396 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: mathematical work | 1396 | Not measured | Only GLM 4.5 Air measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GLM 4.5 Air | Mercury 2 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 81.5% | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: science | 1407 | 1343 | GLM 4.5 Air |
| LMArena Text: medicine and healthcare | 1402 | 1363 | GLM 4.5 Air |
| LMArena Text: legal and government | 1382 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: business and finance | 1371 | 1349 | GLM 4.5 Air |
| Neither model has a result on the other 13 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GLM 4.5 Air | Mercury 2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1146 | 1036 | GLM 4.5 Air |
| Design Arena: UI components | 1120 | 996 | GLM 4.5 Air |
| Design Arena: game dev | 1094 | 984 | GLM 4.5 Air |
| Design Arena: data viz | 1186 | 1009 | GLM 4.5 Air |
| Design Arena: 3D | 1122 | 982 | GLM 4.5 Air |
| Design Arena: all code categories | 1137 | 1021 | GLM 4.5 Air |
| Design Arena: SVG | 1071 | 998 | GLM 4.5 Air |
| Design Arena: ASCII art | Not measured | 988 | Only Mercury 2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index121
| Benchmark | GLM 4.5 Air | Mercury 2 | Leader |
|---|---|---|---|
| IFBench | 37.6% | 69.8% | Mercury 2 |
| Long context reasoning (AA-LCR) | 46.7% | 43.7% | GLM 4.5 Air |
| LMArena Text | 1384 | 1354 | GLM 4.5 Air |
| LMArena Text: instruction following | 1354 | 1328 | GLM 4.5 Air |
| LMArena Text: creative writing | 1343 | 1286 | GLM 4.5 Air |
| LMArena Text: English | 1397 | 1378 | GLM 4.5 Air |
| LMArena Text: non-English | 1366 | 1330 | GLM 4.5 Air |
| LMArena Text: Chinese | 1426 | 1414 | GLM 4.5 Air |
| LMArena Text: German | 1377 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: Japanese | 1348 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: Korean | 1308 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: French | 1400 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: Spanish | 1385 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: Russian | 1373 | 1304 | GLM 4.5 Air |
| LMArena Text: Polish | 1368 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: multi-turn | 1371 | 1357 | GLM 4.5 Air |
| LMArena Text: longer queries | 1366 | 1329 | GLM 4.5 Air |
| LMArena Text: writing and language | 1354 | 1300 | GLM 4.5 Air |
| LMArena Text: entertainment and media | 1339 | 1314 | GLM 4.5 Air |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5 Air | Mercury 2 | 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
Mercury 2 costs less
Repository review
50K input + 3K output tokens
GLM 4.5 Air costs less
Agent loop
200K input + 10K output tokens
GLM 4.5 Air 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 Air | Inception Mercury 2 |
|---|---|---|
| Maker | Z.ai | Inception |
| Weights | Open weights | No public weights listed |
| Released | July 25, 2025 | March 4, 2026 |
| Context window | 131K | 128K |
| Input $/Mtok | $0.20 | $0.25 |
| Output $/Mtok | $1.10 | $0.75 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5 Air better than Mercury 2 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5 Air for a larger context window (131K against 128K tokens) and work measured by Terminal-Bench Hard (Artificial Analysis run) or τ²-Bench Telecom, where it leads.
Which is cheaper, GLM 4.5 Air or Mercury 2?
GLM 4.5 Air costs $0.20 per million input tokens and $1.10 per million output tokens; Mercury 2 costs $0.25 and $0.75. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.5 Air or Mercury 2?
GLM 4.5 Air, with 131K tokens against 128K.
How many benchmarks is the GLM 4.5 Air vs Mercury 2 comparison based on?
33 benchmarks have a result for both models as of October 22, 2026. GLM 4.5 Air's coding index rests on 0 independent benchmarks and Mercury 2's on 1. Where only one model has a result, the page says so instead of naming a winner.