Model comparison
DeepSeek-V3.1 vs Muse Glimmer
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 41.7 on the Noometry Index.
Last verified . 12 shared benchmarks.
DeepSeek
MetaSummary
- They share 12 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 6 categories and Muse Glimmer in 2 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in long context, where Muse Glimmer leads 42.1 to 36.3.
Side by side
| DeepSeek-V3.1 | Muse Glimmer | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.8 | 41.7 |
| Released | 2025-08-21 | 2026-08-10 |
| Weights | Open | Open |
| Context window | 164K | — |
| Max output | 8K | — |
| Input $ / M tokens | $0.25 | — |
| Output $ / M tokens | $0.95 | — |
| Results tracked | 27 | 15 |
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Category by category
Coding Too close to call
DeepSeek-V3.1: 40.3 (#144), Muse Glimmer: 40.4 (#142)
| Benchmark | DeepSeek-V3.1 | Muse Glimmer |
|---|---|---|
| LMArena Coding | 1417 | 1416 |
| LMArena WebDev | — | 1355 |
| SciCode | — | 44.9% |
| WeirdML | 38.4% | — |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.9 (#110), Muse Glimmer: 25.7 (#144)
| Benchmark | DeepSeek-V3.1 | Muse Glimmer |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1396 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| CritPt | — | 2.6% |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |
Math Too close to call
DeepSeek-V3.1: 38.9 (#122), Muse Glimmer: 38.8 (#125)
| Benchmark | DeepSeek-V3.1 | Muse Glimmer |
|---|---|---|
| LMArena Math | 1420 | 1417 |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.7 (#90), Muse Glimmer: 38.5 (#143)
| Benchmark | DeepSeek-V3.1 | Muse Glimmer |
|---|---|---|
| LMArena Expert | 1405 | 1386 |
| Vectara Hallucination Rate | 5.5% | — |
Multilingual DeepSeek-V3.1 leads
DeepSeek-V3.1: 51.6 (#106), Muse Glimmer: 50.5 (#122)
| Benchmark | DeepSeek-V3.1 | Muse Glimmer |
|---|---|---|
| LMArena Non-English | 1400 | 1384 |
| LMArena Chinese | 1469 | 1412 |
| LMArena Russian | 1405 | 1390 |
| LMArena French | 1447 | — |
| LMArena German | 1411 | — |
| LMArena Japanese | 1378 | — |
| LMArena Korean | 1337 | — |
| LMArena Spanish | 1431 | — |
Instruction Following DeepSeek-V3.1 leads
DeepSeek-V3.1: 73.9 (#110), Muse Glimmer: 72.6 (#135)
| Benchmark | DeepSeek-V3.1 | Muse Glimmer |
|---|---|---|
| LMArena Instruction Following | 1400 | 1375 |
Long Context Muse Glimmer leads
DeepSeek-V3.1: 36.3 (#232), Muse Glimmer: 42.1 (#129)
| Benchmark | DeepSeek-V3.1 | Muse Glimmer |
|---|---|---|
| LMArena Longer Query | 1422 | 1382 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference DeepSeek-V3.1 leads
DeepSeek-V3.1: 60.3 (#98), Muse Glimmer: 57.5 (#126)
| Benchmark | DeepSeek-V3.1 | Muse Glimmer |
|---|---|---|
| LMArena Text | 1420 | 1389 |
| LMArena Creative Writing | 1401 | 1339 |
| LMArena Multi-Turn | 1408 | 1399 |
| EQ-Bench Creative Writing | 1436 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than Muse Glimmer?
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 41.7 on the Noometry Index.
Is DeepSeek-V3.1 or Muse Glimmer better for coding?
They score almost the same on coding (40.3 vs 40.4); test both on your own repository before choosing.
How many benchmarks do DeepSeek-V3.1 and Muse Glimmer share?
12 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Muse Glimmer has 15.