Model comparison
GLM-4.7-Flash vs Inkling
Inkling is the stronger model overall, scoring 44.1 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 18× less per token, which makes it the better buy when Inkling's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Summary
- They share 20 benchmarks with published results for both. GLM-4.7-Flash scores higher in 2 categories and Inkling in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Inkling leads 55.1 to 35.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 88.9% for Inkling.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $1.87 / $4.68 for Inkling.
- GLM-4.7-Flash accepts more context: 200K tokens versus 66K.
Side by side
| GLM-4.7-Flash | Inkling | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Thinking Machines Lab |
| Noometry Index | 38.8 | 44.1 |
| Released | 2026-01-19 | 2026-07-15 |
| Weights | Open | Open |
| Context window | 200K | 66K |
| Max output | 131K | 66K |
| Input $ / M tokens | $0.06 | $1.87 |
| Output $ / M tokens | $0.40 | $4.68 |
| Results tracked | 21 | 41 |
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Category by category
Coding GLM-4.7-Flash leads
GLM-4.7-Flash: 40.6 (#135), Inkling: 34.5 (#234)
| Benchmark | GLM-4.7-Flash | Inkling |
|---|---|---|
| LMArena Coding | 1383 | 1464 |
| FrontierCode | — | 14% |
| LMArena WebDev | — | 1413 |
| FrontierSWE | — | 4.1% |
| SciCode | — | 47% |
| WeirdML | — | 32.3% |
| ALE-Bench | — | 946 |
Agentic & Tool Use Not comparable
GLM-4.7-Flash: —, Inkling: 29.6 (#85)
| Benchmark | GLM-4.7-Flash | Inkling |
|---|---|---|
| APEX-Agents | — | 33.8% |
| τ²-bench Banking | — | 25% |
Reasoning Inkling leads
GLM-4.7-Flash: 20.9 (#229), Inkling: 40.4 (#56)
| Benchmark | GLM-4.7-Flash | Inkling |
|---|---|---|
| Chess Puzzles | 0% | 21% |
| LMArena Hard Prompts | 1356 | 1451 |
| ARC-AGI-2 | — | 36.5% |
| SimpleBench | — | 50% |
| ARC-AGI-1 | — | 79.5% |
| CritPt | — | 5.4% |
| DTBench | — | 87.5% |
| LMCA | — | 37.6% |
| Epoch Capabilities Index | — | 148.54 |
Math GLM-4.7-Flash leads
GLM-4.7-Flash: 36.1 (#173), Inkling: 31.3 (#225)
| Benchmark | GLM-4.7-Flash | Inkling |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 88.9% |
| LMArena Math | 1355 | 1479 |
| FrontierMath (Tiers 1-3) | — | 33.3% |
| FrontierMath Tier 4 | — | 4.9% |
| ProofBench | — | 0% |
Knowledge Inkling leads
GLM-4.7-Flash: 35.5 (#184), Inkling: 55.1 (#49)
| Benchmark | GLM-4.7-Flash | Inkling |
|---|---|---|
| GPQA Diamond | 60.5% | 88.3% |
| LMArena Expert | 1357 | 1465 |
| SimpleQA Verified | — | 40.3% |
| Vectara Hallucination Rate | 9.3% | — |
Multilingual Inkling leads
GLM-4.7-Flash: 46.5 (#158), Inkling: 54.0 (#52)
| Benchmark | GLM-4.7-Flash | Inkling |
|---|---|---|
| LMArena Non-English | 1330 | 1434 |
| LMArena Chinese | 1403 | 1490 |
| LMArena French | 1332 | 1458 |
| LMArena German | 1337 | 1446 |
| LMArena Korean | 1283 | 1404 |
| LMArena Russian | 1332 | 1429 |
| LMArena Spanish | 1350 | 1448 |
| LMArena Japanese | — | 1429 |
Instruction Following Inkling leads
GLM-4.7-Flash: 70.1 (#167), Inkling: 75.1 (#71)
| Benchmark | GLM-4.7-Flash | Inkling |
|---|---|---|
| LMArena Instruction Following | 1327 | 1426 |
Long Context Inkling leads
GLM-4.7-Flash: 40.9 (#148), Inkling: 43.8 (#86)
| Benchmark | GLM-4.7-Flash | Inkling |
|---|---|---|
| LMArena Longer Query | 1345 | 1434 |
Writing & Preference Inkling leads
GLM-4.7-Flash: 47.4 (#210), Inkling: 65.2 (#51)
| Benchmark | GLM-4.7-Flash | Inkling |
|---|---|---|
| LMArena Text | 1351 | 1441 |
| LMArena Creative Writing | 1297 | 1387 |
| EQ-Bench Creative Writing | 1125 | 1611 |
| LMArena Multi-Turn | 1342 | 1436 |
| EQ-Bench 4 | — | 1226 |
Frequently asked questions
Is GLM-4.7-Flash better than Inkling?
Inkling is the stronger model overall, scoring 44.1 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 18× less per token, which makes it the better buy when Inkling's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7-Flash or Inkling?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Inkling lists at $1.87 and $4.68.
Is GLM-4.7-Flash or Inkling better for coding?
GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 34.5 in the Noometry coding category.
Which has the bigger context window?
GLM-4.7-Flash does, with 200K tokens against 66K.
How many benchmarks do GLM-4.7-Flash and Inkling share?
20 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Inkling has 41.