Model comparison
DeepSeek-V3.1-Terminus vs GLM-5.3
GLM-5.3 is the stronger model overall, scoring 54.8 to 43.1 on the Noometry Index. DeepSeek-V3.1-Terminus costs 4.8× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
DeepSeekSummary
- They share 15 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 0 categories and GLM-5.3 in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 38.5.
- The biggest single-benchmark swing is LMCA: 28.6% for DeepSeek-V3.1-Terminus and 55.5% for GLM-5.3.
- DeepSeek-V3.1-Terminus is cheaper at $0.27 / $1 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- GLM-5.3 accepts more context: 1M tokens versus 164K.
Side by side
| DeepSeek-V3.1-Terminus | GLM-5.3 | |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 43.1 | 54.8 |
| Released | 2025-09-22 | 2026-08-14 |
| Weights | Open | Open |
| Context window | 164K | 1M |
| Max output | 147K | 131K |
| Input $ / M tokens | $0.27 | $1.40 |
| Output $ / M tokens | $1 | $4.40 |
| Results tracked | 16 | 42 |
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Category by category
Coding GLM-5.3 leads
DeepSeek-V3.1-Terminus: 42.0 (#113), GLM-5.3: 59.5 (#14)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3 |
|---|---|---|
| SciCode | 40.6% | 59% |
| LMArena Coding | 1426 | 1496 |
| ALE-Bench | 745.17 | 1,317 |
| DeepSWE | — | 69% |
| FrontierCode | — | 40.1% |
| CursorBench | — | 42.6% |
| LMArena WebDev | — | 1622 |
| FrontierSWE | — | 30.2% |
| WeirdML | — | 75.4% |
Agentic & Tool Use Not comparable
DeepSeek-V3.1-Terminus: —, GLM-5.3: 36.4 (#38)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3 |
|---|---|---|
| APEX-Agents | — | 56.6% |
| Vending-Bench 2 | — | 8,164 |
Reasoning GLM-5.3 leads
DeepSeek-V3.1-Terminus: 26.4 (#133), GLM-5.3: 46.1 (#46)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3 |
|---|---|---|
| CritPt | 1.7% | 19.1% |
| LMArena Hard Prompts | 1426 | 1489 |
| DTBench | 81.3% | 87.7% |
| LMCA | 28.6% | 55.5% |
| Kagi LLM Benchmark | 57.4% | — |
| NYT Connections (extended) | — | 74.2% |
| Chess Puzzles | — | 21% |
| Mystery Game Puzzles | — | 33% |
| Bench to the Future 3 | — | 0.15 |
| Epoch Capabilities Index | — | 155.61 |
Math GLM-5.3 leads
DeepSeek-V3.1-Terminus: 38.5 (#137), GLM-5.3: 62.3 (#33)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3 |
|---|---|---|
| LMArena Math | 1402 | 1489 |
| FrontierMath (Tiers 1-3) | — | 68.8% |
| FrontierMath Tier 4 | — | 29.3% |
| OTIS Mock AIME 2024-2025 | — | 91.1% |
| ProofBench | — | 49% |
Knowledge Not comparable
DeepSeek-V3.1-Terminus: —, GLM-5.3: 58.3 (#37)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3 |
|---|---|---|
| GPQA Diamond | — | 90.9% |
| SimpleQA Verified | — | 41% |
| LMArena Expert | — | 1516 |
Multilingual GLM-5.3 leads
DeepSeek-V3.1-Terminus: 52.1 (#92), GLM-5.3: 55.7 (#28)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3 |
|---|---|---|
| LMArena Non-English | 1407 | 1457 |
| LMArena Russian | 1436 | 1463 |
| LMArena Chinese | — | 1528 |
| LMArena French | — | 1499 |
| LMArena German | — | 1499 |
| LMArena Japanese | — | 1453 |
| LMArena Korean | — | 1472 |
| LMArena Spanish | — | 1460 |
Instruction Following GLM-5.3 leads
DeepSeek-V3.1-Terminus: 74.0 (#106), GLM-5.3: 77.5 (#23)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3 |
|---|---|---|
| LMArena Instruction Following | 1404 | 1477 |
Long Context GLM-5.3 leads
DeepSeek-V3.1-Terminus: 43.4 (#97), GLM-5.3: 45.4 (#41)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3 |
|---|---|---|
| LMArena Longer Query | 1421 | 1482 |
Writing & Preference GLM-5.3 leads
DeepSeek-V3.1-Terminus: 61.0 (#92), GLM-5.3: 75.7 (#6)
| Benchmark | DeepSeek-V3.1-Terminus | GLM-5.3 |
|---|---|---|
| LMArena Text | 1419 | 1471 |
| LMArena Creative Writing | 1403 | 1457 |
| LMArena Multi-Turn | 1411 | 1472 |
| EQ-Bench Creative Writing | — | 2075 |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than GLM-5.3?
GLM-5.3 is the stronger model overall, scoring 54.8 to 43.1 on the Noometry Index. DeepSeek-V3.1-Terminus costs 4.8× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.1-Terminus or GLM-5.3?
DeepSeek-V3.1-Terminus is cheaper. It lists at $0.27 per million input tokens and $1 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.
Is DeepSeek-V3.1-Terminus or GLM-5.3 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 42.0 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 164K.
How many benchmarks do DeepSeek-V3.1-Terminus and GLM-5.3 share?
15 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and GLM-5.3 has 42.