Model comparison
GLM-4.7-Flash vs Mercury 2.5
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 33.5 on the Noometry Index. Mercury 2.5 costs 2.1× less per token, which makes it the better buy when GLM-4.7-Flash's lead doesn't matter for your workload.
Last verified . 0 shared benchmarks.
Summary
- The widest gap is in math, where GLM-4.7-Flash leads 36.1 to 23.3.
- Mercury 2.5 is cheaper at $0.04 / $0.15 per million input/output tokens, against $0.06 / $0.40 for GLM-4.7-Flash.
- Mercury 2.5 accepts more context: 260K tokens versus 200K.
- GLM-4.7-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7-Flash | Mercury 2.5 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Inception |
| Noometry Index | 38.8 | 33.5 |
| Released | 2026-01-19 | 2026-09-08 |
| Weights | Open | Proprietary |
| Context window | 200K | 260K |
| Max output | 131K | 66K |
| Input $ / M tokens | $0.06 | $0.04 |
| Output $ / M tokens | $0.40 | $0.15 |
| Results tracked | 21 | 4 |
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Category by category
Coding GLM-4.7-Flash leads
GLM-4.7-Flash: 40.6 (#135), Mercury 2.5: 39.5 (#156)
| Benchmark | GLM-4.7-Flash | Mercury 2.5 |
|---|---|---|
| SciCode | — | 38.5% |
| LMArena Coding | 1383 | — |
| ALE-Bench | — | 301.65 |
Reasoning Mercury 2.5 leads
GLM-4.7-Flash: 20.9 (#229), Mercury 2.5: 22.4 (#193)
| Benchmark | GLM-4.7-Flash | Mercury 2.5 |
|---|---|---|
| CritPt | — | 0% |
| Chess Puzzles | 0% | — |
| LMArena Hard Prompts | 1356 | — |
Math GLM-4.7-Flash leads
GLM-4.7-Flash: 36.1 (#173), Mercury 2.5: 23.3 (#272)
| Benchmark | GLM-4.7-Flash | Mercury 2.5 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | — |
| ProofBench | — | 3% |
| LMArena Math | 1355 | — |
Knowledge Not comparable
GLM-4.7-Flash: 35.5 (#184), Mercury 2.5: —
| Benchmark | GLM-4.7-Flash | Mercury 2.5 |
|---|---|---|
| GPQA Diamond | 60.5% | — |
| Vectara Hallucination Rate | 9.3% | — |
| LMArena Expert | 1357 | — |
Multilingual Not comparable
GLM-4.7-Flash: 46.5 (#158), Mercury 2.5: —
| Benchmark | GLM-4.7-Flash | Mercury 2.5 |
|---|---|---|
| LMArena Non-English | 1330 | — |
| LMArena Chinese | 1403 | — |
| LMArena French | 1332 | — |
| LMArena German | 1337 | — |
| LMArena Korean | 1283 | — |
| LMArena Russian | 1332 | — |
| LMArena Spanish | 1350 | — |
Instruction Following Not comparable
GLM-4.7-Flash: 70.1 (#167), Mercury 2.5: —
| Benchmark | GLM-4.7-Flash | Mercury 2.5 |
|---|---|---|
| LMArena Instruction Following | 1327 | — |
Long Context Not comparable
GLM-4.7-Flash: 40.9 (#148), Mercury 2.5: —
| Benchmark | GLM-4.7-Flash | Mercury 2.5 |
|---|---|---|
| LMArena Longer Query | 1345 | — |
Writing & Preference Not comparable
GLM-4.7-Flash: 47.4 (#210), Mercury 2.5: —
| Benchmark | GLM-4.7-Flash | Mercury 2.5 |
|---|---|---|
| LMArena Text | 1351 | — |
| LMArena Creative Writing | 1297 | — |
| EQ-Bench Creative Writing | 1125 | — |
| LMArena Multi-Turn | 1342 | — |
Frequently asked questions
Is GLM-4.7-Flash better than Mercury 2.5?
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 33.5 on the Noometry Index. Mercury 2.5 costs 2.1× less per token, which makes it the better buy when GLM-4.7-Flash's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7-Flash or Mercury 2.5?
Mercury 2.5 is cheaper. It lists at $0.04 per million input tokens and $0.15 per million output tokens; GLM-4.7-Flash lists at $0.06 and $0.40.
Is GLM-4.7-Flash or Mercury 2.5 better for coding?
GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 39.5 in the Noometry coding category.
Which has the bigger context window?
Mercury 2.5 does, with 260K tokens against 200K.
How many benchmarks do GLM-4.7-Flash and Mercury 2.5 share?
0 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Mercury 2.5 has 4.