Model comparison
DeepSeek-V3.1 vs Mercury 2.5
DeepSeek-V3.1 has enough public results to be ranked (#106); Mercury 2.5 does not yet, so treat this comparison as directional.
Last verified . 0 shared benchmarks.
DeepSeekSummary
- The widest gap is in math, where DeepSeek-V3.1 leads 36.1 to 22.4.
- Mercury 2.5 is cheaper at $0.04 / $0.15 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
- Mercury 2.5 accepts more context: 260K tokens versus 164K.
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.1 | Mercury 2.5 | |
|---|---|---|
| Provider | DeepSeek | Inception |
| Noometry Index | 42.2 | 30.2 |
| Released | 2025-08-21 | 2026-09-08 |
| Weights | Open | Proprietary |
| Context window | 164K | 260K |
| Max output | 8K | 66K |
| Input $ / M tokens | $0.25 | $0.04 |
| Output $ / M tokens | $0.95 | $0.15 |
| Results tracked | 27 | 2 |
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Category by category
Coding Not comparable
DeepSeek-V3.1: 39.8 (#140), Mercury 2.5: —
| Benchmark | DeepSeek-V3.1 | Mercury 2.5 |
|---|---|---|
| WeirdML | 38.4% | — |
| LMArena Coding | 1417 | — |
| ALE-Bench | — | 301.65 |
Reasoning Not comparable
DeepSeek-V3.1: 27.3 (#109), Mercury 2.5: —
| Benchmark | DeepSeek-V3.1 | Mercury 2.5 |
|---|---|---|
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| LMArena Hard Prompts | 1417 | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 36.1 (#125), Mercury 2.5: 22.4
| Benchmark | DeepSeek-V3.1 | Mercury 2.5 |
|---|---|---|
| ProofBench | — | 3% |
| LMArena Math | 1420 | — |
Knowledge Not comparable
DeepSeek-V3.1: 43.0 (#86), Mercury 2.5: —
| Benchmark | DeepSeek-V3.1 | Mercury 2.5 |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | — |
| LMArena Expert | 1403 | — |
Multilingual Not comparable
DeepSeek-V3.1: 51.8 (#100), Mercury 2.5: —
| Benchmark | DeepSeek-V3.1 | Mercury 2.5 |
|---|---|---|
| LMArena Non-English | 1400 | — |
| LMArena Chinese | 1470 | — |
| LMArena French | 1447 | — |
| LMArena German | 1412 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1337 | — |
| LMArena Russian | 1405 | — |
| LMArena Spanish | 1429 | — |
Instruction Following Not comparable
DeepSeek-V3.1: 74.1 (#105), Mercury 2.5: —
| Benchmark | DeepSeek-V3.1 | Mercury 2.5 |
|---|---|---|
| LMArena Instruction Following | 1400 | — |
Long Context Not comparable
DeepSeek-V3.1: 36.3 (#227), Mercury 2.5: —
| Benchmark | DeepSeek-V3.1 | Mercury 2.5 |
|---|---|---|
| Fiction.LiveBench | 52.8% | — |
| LMArena Longer Query | 1422 | — |
Writing & Preference Not comparable
DeepSeek-V3.1: 60.5 (#93), Mercury 2.5: —
| Benchmark | DeepSeek-V3.1 | Mercury 2.5 |
|---|---|---|
| LMArena Text | 1420 | — |
| LMArena Creative Writing | 1401 | — |
| EQ-Bench Creative Writing | 1436 | — |
| LMArena Multi-Turn | 1408 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than Mercury 2.5?
DeepSeek-V3.1 has enough public results to be ranked (#106); Mercury 2.5 does not yet, so treat this comparison as directional.
Which is cheaper, DeepSeek-V3.1 or Mercury 2.5?
Mercury 2.5 is cheaper. It lists at $0.04 per million input tokens and $0.15 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.
Which has the bigger context window?
Mercury 2.5 does, with 260K tokens against 164K.
How many benchmarks do DeepSeek-V3.1 and Mercury 2.5 share?
0 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Mercury 2.5 has 2.