GLM 4.5V vs Mercury 2.5
Mercury 2.5 leads GLM 4.5V on the Artificial Analysis Intelligence Index, 12.3 to 7.6, and Mercury 2.5 costs 12.0x less per output token ($0.15 vs $1.80 per million). Updated October 21, 2026; 3 benchmarks have a result for both.
GLM 4.5V or Mercury 2.5: which to pick
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; GLM 4.5V has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 3
- GLM 4.5V only
- 38
- Mercury 2.5 only
- 19
- Like-for-like categories
- 3 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 Mercury 2.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Mercury 2.5
Mercury 2.5 leads, 12.3 against 7.6.
Same board, both measured
Long documents
Listed context window
Mercury 2.5
Mercury 2.5 takes 260K tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2.5
GLM 4.5V $0.0015, Mercury 2.5 $0.0001 per turn.
List prices
At a glance
- Coding index
- GLM 4.5V not benchmarked, Mercury 2.5 1.6 from 2 benchmarks.
- Widest category
- General, 71.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): GLM 4.5V 0.0% against Mercury 2.5 71.7%, 71.7 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5V $1.80 against Mercury 2.5 $0.15 per million output tokens, 12.0x apart.
- Context window
- GLM 4.5V takes 66K tokens, Mercury 2.5 260K.
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 4.5V | Bars | Mercury 2.5 | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 3 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index13 boards, none shared
| Benchmark | GLM 4.5V | Mercury 2.5 | Leader |
|---|---|---|---|
| Terminal-Bench 4 (Vals AI) | Not measured | 0.0% | Only Mercury 2.5 measured |
| Vibe Code Bench (Vals AI) | Not measured | 3.7% | Only Mercury 2.5 measured |
| Code migration (Vals AI) | Not measured | 4.5% | Only Mercury 2.5 measured |
| IOI (Vals AI) | Not measured | 2.4% | Only Mercury 2.5 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 34.5% | Only Mercury 2.5 measured |
| SkillsBench (Vals AI) | Not measured | 18.1% | Only Mercury 2.5 measured |
| CyberBench (Vals AI) | Not measured | 45.5% | Only Mercury 2.5 measured |
| LiveCodeBench (Artificial Analysis run) | 60.4% | Not measured | Only GLM 4.5V measured |
| SciCode | Not measured | 38.5% | Only Mercury 2.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 6.8% | Not measured | Only GLM 4.5V measured |
| ALE-Bench | Not measured | 301.6 | Only Mercury 2.5 measured |
| LMArena Text: coding | 1347 | Not measured | Only GLM 4.5V measured |
| LMArena Text: software and IT | 1353 | Not measured | Only GLM 4.5V measured |
| 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 4.5V | Mercury 2.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.6 | 12.3 | Mercury 2.5 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GLM 4.5V | Mercury 2.5 | Leader |
|---|---|---|---|
| Finance Agent (Vals AI) | Not measured | 18.9% | Only Mercury 2.5 measured |
| Tax Agent Bench (Vals AI) | Not measured | 2.5% | Only Mercury 2.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 4.3% | Only Mercury 2.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 8.8% | Only Mercury 2.5 measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 0.0% | Only Mercury 2.5 measured |
| τ²-Bench Telecom | 22.5% | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5V | Mercury 2.5 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 68.4% | Not measured | Only GLM 4.5V measured |
| Humanity's Last Exam (Artificial Analysis run) | 6.3% | 11.8% | Mercury 2.5 |
| LMArena Text: hard prompts | 1335 | Not measured | Only GLM 4.5V measured |
| LMArena Text: expert | 1353 | Not measured | Only GLM 4.5V measured |
| LMArena Text: hard prompts in English | 1357 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: diagrams | 1151 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: homework | 1163 | Not measured | Only GLM 4.5V measured |
| Kagi LLM Benchmark | 59.8% | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GLM 4.5V | Mercury 2.5 | Leader |
|---|---|---|---|
| AA Math Index | 73.0 | Not measured | Only GLM 4.5V measured |
| AIME 2025 (Artificial Analysis run) | 73.0% | Not measured | Only GLM 4.5V measured |
| ProofBench | Not measured | 3.0% | Only Mercury 2.5 measured |
| LMArena Text: math | 1354 | Not measured | Only GLM 4.5V measured |
| LMArena Text: mathematical work | 1336 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.5V | Mercury 2.5 | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | Not measured | 83.1% | Only Mercury 2.5 measured |
| MedCode (Vals AI) | Not measured | 31.3% | Only Mercury 2.5 measured |
| MMLU Pro (Artificial Analysis run) | 78.8% | Not measured | Only GLM 4.5V measured |
| LMArena Text: science | 1330 | Not measured | Only GLM 4.5V measured |
| LMArena Text: medicine and healthcare | 1349 | Not measured | Only GLM 4.5V measured |
| LMArena Text: legal and government | 1338 | Not measured | Only GLM 4.5V measured |
| LMArena Text: business and finance | 1309 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 11 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5V | Mercury 2.5 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5V | Mercury 2.5 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 45.3% | Only Mercury 2.5 measured |
| MedScribe (Vals AI) | Not measured | 55.1% | Only Mercury 2.5 measured |
| IFBench | 34.2% | Not measured | Only GLM 4.5V measured |
| Long context reasoning (AA-LCR) | 0.0% | 71.7% | Mercury 2.5 |
| LMArena Text | 1333 | Not measured | Only GLM 4.5V measured |
| LMArena Text: instruction following | 1311 | Not measured | Only GLM 4.5V measured |
| LMArena Text: creative writing | 1294 | Not measured | Only GLM 4.5V measured |
| LMArena Vision | 1154 | Not measured | Only GLM 4.5V measured |
| LMArena Text: English | 1362 | Not measured | Only GLM 4.5V measured |
| LMArena Text: non-English | 1303 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Chinese | 1337 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Spanish | 1336 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Russian | 1298 | Not measured | Only GLM 4.5V measured |
| LMArena Text: Polish | 1302 | Not measured | Only GLM 4.5V measured |
| LMArena Text: multi-turn | 1333 | Not measured | Only GLM 4.5V measured |
| LMArena Text: longer queries | 1305 | Not measured | Only GLM 4.5V measured |
| LMArena Text: writing and language | 1302 | Not measured | Only GLM 4.5V measured |
| LMArena Text: entertainment and media | 1303 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: English | 1183 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: reading text | 1151 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5V | Mercury 2.5 | 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 2.5 costs less
Repository review
50K input + 3K output tokens
Mercury 2.5 costs less
Agent loop
200K input + 10K output tokens
Mercury 2.5 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 4.5V | Inception Mercury 2.5 |
|---|---|---|
| Maker | Z.ai | Inception |
| Weights | Open weights | No public weights listed |
| Released | August 11, 2025 | September 8, 2026 |
| Context window | 66K | 260K |
| Input $/Mtok | $0.60 | $0.04 |
| Output $/Mtok | $1.80 | $0.15 |
| Coding index | Not benchmarked | 1.6 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5V better than Mercury 2.5 for coding?
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; GLM 4.5V has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GLM 4.5V or Mercury 2.5?
GLM 4.5V costs $0.60 per million input tokens and $1.80 per million output tokens; Mercury 2.5 costs $0.04 and $0.15. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.5V or Mercury 2.5?
Mercury 2.5, with 260K tokens against 66K.
How many benchmarks is the GLM 4.5V vs Mercury 2.5 comparison based on?
3 benchmarks have a result for both models as of October 21, 2026. GLM 4.5V's coding index rests on 0 independent benchmarks and Mercury 2.5's on 2. Where only one model has a result, the page says so instead of naming a winner.