Model comparison
DeepSeek-V3.1-Terminus vs GLM-4.7-Flash
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 3.1× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.
Last verified . 10 shared benchmarks.
DeepSeekSummary
- They share 10 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 7 categories and GLM-4.7-Flash in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.0 to 47.4.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.27 / $1 for DeepSeek-V3.1-Terminus.
- GLM-4.7-Flash accepts more context: 200K tokens versus 164K.
Side by side
| DeepSeek-V3.1-Terminus | GLM-4.7-Flash | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 43.1 | 38.8 |
| Released | 2025-09-22 | 2026-01-19 |
| Weights | Open | Open |
| Context window | 164K | 200K |
| Max output | 147K | 131K |
| Input $ / M tokens | $0.27 | $0.06 |
| Output $ / M tokens | $1 | $0.40 |
| Results tracked | 16 | 21 |
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Category by category
Coding DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 42.0 (#113), GLM-4.7-Flash: 40.6 (#135)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-4.7-Flash |
|---|---|---|
| LMArena Coding | 1426 | 1383 |
| SciCode | 40.6% | — |
| ALE-Bench | 745.17 | — |
Reasoning DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 26.4 (#133), GLM-4.7-Flash: 20.9 (#229)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-4.7-Flash |
|---|---|---|
| LMArena Hard Prompts | 1426 | 1356 |
| Kagi LLM Benchmark | 57.4% | — |
| CritPt | 1.7% | — |
| Chess Puzzles | — | 0% |
| DTBench | 81.3% | — |
| LMCA | 28.6% | — |
Math DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 38.5 (#137), GLM-4.7-Flash: 36.1 (#173)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-4.7-Flash |
|---|---|---|
| LMArena Math | 1402 | 1355 |
| OTIS Mock AIME 2024-2025 | — | 58.3% |
Knowledge Not comparable
DeepSeek-V3.1-Terminus: —, GLM-4.7-Flash: 35.5 (#184)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-4.7-Flash |
|---|---|---|
| GPQA Diamond | — | 60.5% |
| Vectara Hallucination Rate | — | 9.3% |
| LMArena Expert | — | 1357 |
Multilingual DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 52.1 (#92), GLM-4.7-Flash: 46.5 (#158)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-4.7-Flash |
|---|---|---|
| LMArena Non-English | 1407 | 1330 |
| LMArena Russian | 1436 | 1332 |
| LMArena Chinese | — | 1403 |
| LMArena French | — | 1332 |
| LMArena German | — | 1337 |
| LMArena Korean | — | 1283 |
| LMArena Spanish | — | 1350 |
Instruction Following DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 74.0 (#106), GLM-4.7-Flash: 70.1 (#167)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-4.7-Flash |
|---|---|---|
| LMArena Instruction Following | 1404 | 1327 |
Long Context DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 43.4 (#97), GLM-4.7-Flash: 40.9 (#148)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-4.7-Flash |
|---|---|---|
| LMArena Longer Query | 1421 | 1345 |
Writing & Preference DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 61.0 (#92), GLM-4.7-Flash: 47.4 (#210)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-4.7-Flash |
|---|---|---|
| LMArena Text | 1419 | 1351 |
| LMArena Creative Writing | 1403 | 1297 |
| LMArena Multi-Turn | 1411 | 1342 |
| EQ-Bench Creative Writing | — | 1125 |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than GLM-4.7-Flash?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 3.1× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.1-Terminus or GLM-4.7-Flash?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; DeepSeek-V3.1-Terminus lists at $0.27 and $1.
Is DeepSeek-V3.1-Terminus or GLM-4.7-Flash better for coding?
DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 40.6 in the Noometry coding category.
Which has the bigger context window?
GLM-4.7-Flash does, with 200K tokens against 164K.
How many benchmarks do DeepSeek-V3.1-Terminus and GLM-4.7-Flash share?
10 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and GLM-4.7-Flash has 21.