Model comparison
DeepSeek-V3.1-Terminus vs GLM-5.3-Flash
GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 42.6 on the Noometry Index.
Last verified . 13 shared benchmarks.
DeepSeekSummary
- They share 13 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 0 categories and GLM-5.3-Flash in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3-Flash leads 53.1 to 35.7.
- The biggest single-benchmark swing is CritPt: 1.7% for DeepSeek-V3.1-Terminus and 15.4% for GLM-5.3-Flash.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.30 / $1 for DeepSeek-V3.1-Terminus.
- GLM-5.3-Flash accepts more context: 1M tokens versus 164K.
Side by side
| DeepSeek-V3.1-Terminus | GLM-5.3-Flash | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 42.6 | 49.6 |
| Released | 2025-09-22 | 2026-08-20 |
| Weights | Open | Open |
| Context window | 164K | 1M |
| Max output | 66K | 131K |
| Input $ / M tokens | $0.30 | $0.15 |
| Output $ / M tokens | $1 | $0.50 |
| Results tracked | 16 | 36 |
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Category by category
Coding GLM-5.3-Flash leads
DeepSeek-V3.1-Terminus: 41.5 (#114), GLM-5.3-Flash: 53.7 (#26)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3-Flash |
|---|---|---|
| SciCode | 40.6% | 51.6% |
| LMArena Coding | 1426 | 1506 |
| ALE-Bench | 745.17 | 303.55 |
| DeepSWE | — | 63.4% |
| FrontierCode | — | 31.8% |
| LMArena WebDev | — | 1614 |
Agentic & Tool Use Not comparable
DeepSeek-V3.1-Terminus: —, GLM-5.3-Flash: 34.3 (#41)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | — | 52.8% |
| GDP.pdf | — | 14% |
Reasoning GLM-5.3-Flash leads
DeepSeek-V3.1-Terminus: 25.7 (#126), GLM-5.3-Flash: 31.7 (#69)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3-Flash |
|---|---|---|
| CritPt | 1.7% | 15.4% |
| LMArena Hard Prompts | 1427 | 1490 |
| Kagi LLM Benchmark | 57.4% | — |
| Chess Puzzles | — | 14% |
| Mystery Game Puzzles | — | 8% |
| DTBench | 81.3% | — |
| LMCA | 28.6% | — |
| Surface Evolver Bench | — | 52.5% |
| Bench to the Future 3 | — | 0.15 |
| Epoch Capabilities Index | — | 151.9 |
Math GLM-5.3-Flash leads
DeepSeek-V3.1-Terminus: 35.7 (#138), GLM-5.3-Flash: 53.1 (#38)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3-Flash |
|---|---|---|
| LMArena Math | 1402 | 1499 |
| FrontierMath (Tiers 1-3) | — | 55.8% |
| FrontierMath Tier 4 | — | 17.1% |
| OTIS Mock AIME 2024-2025 | — | 93.9% |
| ProofBench | — | 21% |
Knowledge Not comparable
DeepSeek-V3.1-Terminus: —, GLM-5.3-Flash: 58.4 (#33)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3-Flash |
|---|---|---|
| GPQA Diamond | — | 90.2% |
| LMArena Expert | — | 1512 |
Multimodal Not comparable
DeepSeek-V3.1-Terminus: —, GLM-5.3-Flash: 42.6 (#25)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | — | 1304 |
Multilingual GLM-5.3-Flash leads
DeepSeek-V3.1-Terminus: 52.3 (#87), GLM-5.3-Flash: 56.2 (#24)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1407 | 1462 |
| LMArena Russian | 1434 | 1472 |
| LMArena Chinese | — | 1529 |
| LMArena French | — | 1495 |
| LMArena German | — | 1457 |
| LMArena Japanese | — | 1420 |
| LMArena Korean | — | 1443 |
| LMArena Spanish | — | 1471 |
Instruction Following GLM-5.3-Flash leads
DeepSeek-V3.1-Terminus: 74.4 (#96), GLM-5.3-Flash: 77.8 (#17)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1406 | 1480 |
Long Context GLM-5.3-Flash leads
DeepSeek-V3.1-Terminus: 43.5 (#88), GLM-5.3-Flash: 45.3 (#41)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1426 | 1479 |
Writing & Preference GLM-5.3-Flash leads
DeepSeek-V3.1-Terminus: 61.3 (#87), GLM-5.3-Flash: 65.4 (#49)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1420 | 1471 |
| LMArena Creative Writing | 1404 | 1440 |
| LMArena Multi-Turn | 1411 | 1471 |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than GLM-5.3-Flash?
GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 42.6 on the Noometry Index.
Which is cheaper, DeepSeek-V3.1-Terminus or GLM-5.3-Flash?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; DeepSeek-V3.1-Terminus lists at $0.30 and $1.
Is DeepSeek-V3.1-Terminus or GLM-5.3-Flash better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 41.5 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 164K.
How many benchmarks do DeepSeek-V3.1-Terminus and GLM-5.3-Flash share?
13 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and GLM-5.3-Flash has 36.