GLM 4.7 Flash vs Mercury 2.5
GLM 4.7 Flash leads Mercury 2.5 on the Artificial Analysis Intelligence Index, 14.9 to 12.3, and Mercury 2.5 costs 2.7x less per output token ($0.15 vs $0.40 per million). Updated October 21, 2026; 3 benchmarks have a result for both.
GLM 4.7 Flash or Mercury 2.5: which to pick
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding.
Pick GLM 4.7 Flash for work measured by AA Intelligence Index, where it leads.
- Shared results
- 3
- GLM 4.7 Flash only
- 40
- 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
GLM 4.7 Flash
GLM 4.7 Flash leads, 14.9 against 12.3.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 4.7 Flash has a Design Arena: websites result, 1194.
Directional only
Long documents
Listed context window
Mercury 2.5
Mercury 2.5 takes 260K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2.5
GLM 4.7 Flash $0.0003, Mercury 2.5 $0.0001 per turn.
List prices
At a glance
- Coding index
- GLM 4.7 Flash not benchmarked, Mercury 2.5 1.6 from 2 benchmarks.
- Widest category
- General, 30.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): GLM 4.7 Flash 41.7% against Mercury 2.5 71.7%, 30.0 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 Flash $0.40 against Mercury 2.5 $0.15 per million output tokens, 2.7x apart.
- Context window
- GLM 4.7 Flash takes 200K 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 4.7 Flash | Bars | Mercury 2.5 | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 1 | 2 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index12 boards, none shared
| Benchmark | GLM 4.7 Flash | 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 |
| SciCode | Not measured | 38.5% | Only Mercury 2.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | Not measured | Only GLM 4.7 Flash measured |
| ALE-Bench | Not measured | 301.6 | Only Mercury 2.5 measured |
| LMArena Text: coding | 1382 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: software and IT | 1389 | Not measured | Only GLM 4.7 Flash 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 4.7 Flash | Mercury 2.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.9 | 12.3 | GLM 4.7 Flash |
| 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 4.7 Flash | 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 | 98.8% | Not measured | Only GLM 4.7 Flash 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 4.7 Flash | Mercury 2.5 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 58.1% | Not measured | Only GLM 4.7 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 7.6% | 11.8% | Mercury 2.5 |
| GPQA Diamond (Epoch AI run) | 60.5% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: hard prompts | 1356 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: expert | 1357 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: hard prompts in English | 1369 | Not measured | Only GLM 4.7 Flash 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 4.7 Flash | Mercury 2.5 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 58.3% | Not measured | Only GLM 4.7 Flash measured |
| ProofBench | Not measured | 3.0% | Only Mercury 2.5 measured |
| LMArena Text: math | 1355 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: mathematical work | 1369 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GLM 4.7 Flash | 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 |
| LMArena Text: science | 1358 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: medicine and healthcare | 1343 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: legal and government | 1357 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: business and finance | 1343 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.7 Flash | Mercury 2.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1194 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: UI components | 1208 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: game dev | 1135 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: data viz | 1123 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: 3D | 1121 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: all code categories | 1178 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: SVG | 1038 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.7 Flash | 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 | 60.8% | Not measured | Only GLM 4.7 Flash measured |
| Long context reasoning (AA-LCR) | 41.7% | 71.7% | Mercury 2.5 |
| LMArena Text | 1351 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: instruction following | 1327 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: creative writing | 1296 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: English | 1373 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: non-English | 1330 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Chinese | 1403 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: German | 1336 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Korean | 1283 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: French | 1332 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Spanish | 1350 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Russian | 1333 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Polish | 1291 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: multi-turn | 1342 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: longer queries | 1345 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: writing and language | 1320 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: entertainment and media | 1304 | Not measured | Only GLM 4.7 Flash 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 4.7 Flash | 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 4.7 Flash | Inception Mercury 2.5 |
|---|---|---|
| Maker | Z.ai | Inception |
| Weights | Open weights | No public weights listed |
| Released | January 19, 2026 | September 8, 2026 |
| Context window | 200K | 260K |
| Input $/Mtok | $0.0653 | $0.04 |
| Output $/Mtok | $0.40 | $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 4.7 Flash better than Mercury 2.5 for coding?
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding. Pick GLM 4.7 Flash for work measured by AA Intelligence Index, where it leads.
Which is cheaper, GLM 4.7 Flash or Mercury 2.5?
GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 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 4.7 Flash or Mercury 2.5?
Mercury 2.5, with 260K tokens against 200K.
How many benchmarks is the GLM 4.7 Flash vs Mercury 2.5 comparison based on?
3 benchmarks have a result for both models as of October 21, 2026. GLM 4.7 Flash'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.