Model comparison
DeepSeek-V3 vs Mercury 2
DeepSeek-V3 and Mercury 2 score almost the same on the Noometry Index (39.5 vs 39.1), so choose on price, context window or the category you care about most.
Last verified . 15 shared benchmarks.
DeepSeekSummary
- They share 15 benchmarks with published results for both. DeepSeek-V3 scores higher in 5 categories and Mercury 2 in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in coding, where DeepSeek-V3 leads 42.3 to 33.5.
- The biggest single-benchmark swing is WeirdML: 36.1% for DeepSeek-V3 and 43.2% for Mercury 2.
- Mercury 2 is cheaper at $0.25 / $0.75 per million input/output tokens, against $0.24 / $0.90 for DeepSeek-V3.
- DeepSeek-V3 accepts more context: 164K tokens versus 128K.
- DeepSeek-V3 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3 | Mercury 2 | |
|---|---|---|
| Provider | DeepSeek | Inception |
| Noometry Index | 39.5 | 39.1 |
| Released | 2024-12-26 | 2026-02-20 |
| Weights | Open | Proprietary |
| Context window | 164K | 128K |
| Max output | 164K | 50K |
| Input $ / M tokens | $0.24 | $0.25 |
| Output $ / M tokens | $0.90 | $0.75 |
| Results tracked | 60 | 17 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding DeepSeek-V3 leads
DeepSeek-V3: 42.3 (#106), Mercury 2: 33.5 (#255)
| Benchmark | DeepSeek-V3 | Mercury 2 |
|---|---|---|
| SciCode | 35.8% | 38.7% |
| WeirdML | 36.1% | 43.2% |
| LMArena Coding | 1368 | 1391 |
| Aider Polyglot | 55.1% | — |
| LMArena WebDev | — | 1171 |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| ALE-Bench | — | 785.58 |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, Mercury 2: —
| Benchmark | DeepSeek-V3 | Mercury 2 |
|---|---|---|
| METR Time Horizons | 49.6% | — |
Reasoning Mercury 2 leads
DeepSeek-V3: 20.5 (#236), Mercury 2: 23.8 (#170)
| Benchmark | DeepSeek-V3 | Mercury 2 |
|---|---|---|
| CritPt | 0% | 0.8% |
| LMArena Hard Prompts | 1365 | 1362 |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| LiveBench Reasoning | 65.8% | — |
| DTBench | 64.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| LMCA | 15.5% | — |
| BIG-Bench Hard | 87.5% | — |
| Epoch Capabilities Index | 135.94 | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |
Math Not comparable
DeepSeek-V3: 32.1 (#219), Mercury 2: —
| Benchmark | DeepSeek-V3 | Mercury 2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | — |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| LMArena Math | 1373 | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge DeepSeek-V3 leads
DeepSeek-V3: 37.5 (#155), Mercury 2: 36.2 (#172)
| Benchmark | DeepSeek-V3 | Mercury 2 |
|---|---|---|
| Vectara Hallucination Rate | 6.1% | 12.3% |
| LMArena Expert | 1351 | 1358 |
| GPQA Diamond | 67.6% | — |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |
Multilingual DeepSeek-V3 leads
DeepSeek-V3: 48.5 (#143), Mercury 2: 46.6 (#157)
| Benchmark | DeepSeek-V3 | Mercury 2 |
|---|---|---|
| LMArena Non-English | 1358 | 1331 |
| LMArena Chinese | 1391 | 1417 |
| LMArena Russian | 1373 | 1304 |
| LMArena French | 1385 | — |
| LMArena German | 1374 | — |
| LMArena Japanese | 1333 | — |
| LMArena Korean | 1319 | — |
| LMArena Spanish | 1358 | — |
Instruction Following DeepSeek-V3 leads
DeepSeek-V3: 72.8 (#130), Mercury 2: 70.2 (#165)
| Benchmark | DeepSeek-V3 | Mercury 2 |
|---|---|---|
| LMArena Instruction Following | 1345 | 1329 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |
Long Context Mercury 2 leads
DeepSeek-V3: 34.0 (#253), Mercury 2: 40.5 (#154)
| Benchmark | DeepSeek-V3 | Mercury 2 |
|---|---|---|
| LMArena Longer Query | 1352 | 1330 |
| Fiction.LiveBench | 50% | — |
Writing & Preference DeepSeek-V3 leads
DeepSeek-V3: 57.4 (#130), Mercury 2: 53.8 (#155)
| Benchmark | DeepSeek-V3 | Mercury 2 |
|---|---|---|
| LMArena Text | 1375 | 1355 |
| LMArena Creative Writing | 1364 | 1289 |
| LMArena Multi-Turn | 1389 | 1358 |
| Short-Story Creative Writing | 77% | — |
| EQ-Bench Creative Writing | 1472 | — |
| WildBench | 83% | — |
| LiveBench Language | 49.1% | — |
Frequently asked questions
Is DeepSeek-V3 better than Mercury 2?
DeepSeek-V3 and Mercury 2 score almost the same on the Noometry Index (39.5 vs 39.1), so choose on price, context window or the category you care about most.
Which is cheaper, DeepSeek-V3 or Mercury 2?
Mercury 2 is cheaper. It lists at $0.25 per million input tokens and $0.75 per million output tokens; DeepSeek-V3 lists at $0.24 and $0.90.
Is DeepSeek-V3 or Mercury 2 better for coding?
DeepSeek-V3 scores higher on coding benchmarks: 42.3 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3 does, with 164K tokens against 128K.
How many benchmarks do DeepSeek-V3 and Mercury 2 share?
15 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Mercury 2 has 17.