Model comparison
DeepSeek-V3.1-Terminus vs Sonar
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 37.8 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
PerplexitySummary
- The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.3 to 52.6.
- DeepSeek-V3.1-Terminus is cheaper at $0.30 / $1 per million input/output tokens, against $1 / $1 for Sonar.
- DeepSeek-V3.1-Terminus accepts more context: 164K tokens versus 128K.
- DeepSeek-V3.1-Terminus has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.1-Terminus | Sonar | |
|---|---|---|
| Provider | DeepSeek | Perplexity |
| Noometry Index | 42.6 | 37.8 |
| Released | 2025-09-22 | 2024-01-01 |
| Weights | Open | Proprietary |
| Context window | 164K | 128K |
| Max output | 66K | 4K |
| Input $ / M tokens | $0.30 | $1 |
| Output $ / M tokens | $1 | $1 |
| Results tracked | 16 | 7 |
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Category by category
Coding DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 41.5 (#114), Sonar: 35.1 (#217)
| Benchmark | DeepSeek-V3.1-Terminus | Sonar |
|---|---|---|
| SciCode | 40.6% | — |
| LiveBench Coding | — | 35.1% |
| LMArena Coding | 1426 | — |
| ALE-Bench | 745.17 | — |
Reasoning DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 25.7 (#126), Sonar: 20.3 (#221)
| Benchmark | DeepSeek-V3.1-Terminus | Sonar |
|---|---|---|
| Kagi LLM Benchmark | 57.4% | — |
| CritPt | 1.7% | — |
| LiveBench Reasoning | — | 46.3% |
| LMArena Hard Prompts | 1427 | — |
| DTBench | 81.3% | — |
| LiveBench Data Analysis | — | 37.9% |
| LMCA | 28.6% | — |
| LiveBench | — | 46.9% |
Math DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 35.7 (#138), Sonar: 31.3 (#197)
| Benchmark | DeepSeek-V3.1-Terminus | Sonar |
|---|---|---|
| LiveBench Math | — | 41.6% |
| LMArena Math | 1402 | — |
Multilingual Not comparable
DeepSeek-V3.1-Terminus: 52.3 (#87), Sonar: —
| Benchmark | DeepSeek-V3.1-Terminus | Sonar |
|---|---|---|
| LMArena Non-English | 1407 | — |
| LMArena Russian | 1434 | — |
Instruction Following DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 74.4 (#96), Sonar: 71.5 (#146)
| Benchmark | DeepSeek-V3.1-Terminus | Sonar |
|---|---|---|
| LiveBench Instruction Following | — | 76.2% |
| LMArena Instruction Following | 1406 | — |
Long Context Not comparable
DeepSeek-V3.1-Terminus: 43.5 (#88), Sonar: —
| Benchmark | DeepSeek-V3.1-Terminus | Sonar |
|---|---|---|
| LMArena Longer Query | 1426 | — |
Writing & Preference DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 61.3 (#87), Sonar: 52.6 (#166)
| Benchmark | DeepSeek-V3.1-Terminus | Sonar |
|---|---|---|
| LMArena Text | 1420 | — |
| LMArena Creative Writing | 1404 | — |
| LMArena Multi-Turn | 1411 | — |
| LiveBench Language | — | 44.1% |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than Sonar?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 37.8 on the Noometry Index.
Which is cheaper, DeepSeek-V3.1-Terminus or Sonar?
DeepSeek-V3.1-Terminus is cheaper. It lists at $0.30 per million input tokens and $1 per million output tokens; Sonar lists at $1 and $1.
Is DeepSeek-V3.1-Terminus or Sonar better for coding?
DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 41.5 versus 35.1 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.1-Terminus does, with 164K tokens against 128K.
How many benchmarks do DeepSeek-V3.1-Terminus and Sonar share?
0 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Sonar has 7.