Model comparison
DeepSeek-V3.2-Exp vs Mercury 2
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 39.1 on the Noometry Index.
Last verified . 16 shared benchmarks.
DeepSeekSummary
- They share 16 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 6 categories and Mercury 2 in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.7 to 36.2.
- The biggest single-benchmark swing is Vectara Hallucination Rate: 5.3% for DeepSeek-V3.2-Exp and 12.3% for Mercury 2.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $0.25 / $0.75 for Mercury 2.
- DeepSeek-V3.2-Exp accepts more context: 164K tokens versus 128K.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Exp | Mercury 2 | |
|---|---|---|
| Provider | DeepSeek | Inception |
| Noometry Index | 44.3 | 39.1 |
| Released | 2025-09-29 | 2026-02-20 |
| Weights | Open | Proprietary |
| Context window | 164K | 128K |
| Max output | 66K | 50K |
| Input $ / M tokens | $0.26 | $0.25 |
| Output $ / M tokens | $0.38 | $0.75 |
| Results tracked | 49 | 17 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.5 (#65), Mercury 2: 33.5 (#255)
| Benchmark | DeepSeek-V3.2-Exp | Mercury 2 |
|---|---|---|
| LMArena WebDev | 1362 | 1171 |
| SciCode | 38.9% | 38.7% |
| WeirdML | 39.5% | 43.2% |
| LMArena Coding | 1454 | 1391 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| SWE-bench Multilingual | 59% | — |
| ALE-Bench | — | 785.58 |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 32.7 (#59), Mercury 2: —
| Benchmark | DeepSeek-V3.2-Exp | Mercury 2 |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| APEX-Agents | 21.3% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning Mercury 2 leads
DeepSeek-V3.2-Exp: 22.1 (#208), Mercury 2: 23.8 (#170)
| Benchmark | DeepSeek-V3.2-Exp | Mercury 2 |
|---|---|---|
| CritPt | 2.9% | 0.8% |
| LMArena Hard Prompts | 1434 | 1362 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.27 | — |
Math Not comparable
DeepSeek-V3.2-Exp: 41.7 (#87), Mercury 2: —
| Benchmark | DeepSeek-V3.2-Exp | Mercury 2 |
|---|---|---|
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| LMArena Math | 1435 | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.7 (#66), Mercury 2: 36.2 (#172)
| Benchmark | DeepSeek-V3.2-Exp | Mercury 2 |
|---|---|---|
| Vectara Hallucination Rate | 5.3% | 12.3% |
| LMArena Expert | 1436 | 1358 |
| GPQA Diamond | 83.4% | — |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.2 (#90), Mercury 2: 46.6 (#157)
| Benchmark | DeepSeek-V3.2-Exp | Mercury 2 |
|---|---|---|
| LMArena Non-English | 1409 | 1331 |
| LMArena Chinese | 1461 | 1417 |
| LMArena Russian | 1424 | 1304 |
| LMArena French | 1433 | — |
| LMArena German | 1440 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1371 | — |
| LMArena Spanish | 1440 | — |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.5 (#93), Mercury 2: 70.2 (#165)
| Benchmark | DeepSeek-V3.2-Exp | Mercury 2 |
|---|---|---|
| LMArena Instruction Following | 1413 | 1329 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#16), Mercury 2: 40.5 (#154)
| Benchmark | DeepSeek-V3.2-Exp | Mercury 2 |
|---|---|---|
| LMArena Longer Query | 1428 | 1330 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 62.4 (#77), Mercury 2: 53.8 (#155)
| Benchmark | DeepSeek-V3.2-Exp | Mercury 2 |
|---|---|---|
| LMArena Text | 1425 | 1355 |
| LMArena Creative Writing | 1403 | 1289 |
| LMArena Multi-Turn | 1427 | 1358 |
| EQ-Bench Creative Writing | 1515 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Mercury 2?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 39.1 on the Noometry Index.
Which is cheaper, DeepSeek-V3.2-Exp or Mercury 2?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; Mercury 2 lists at $0.25 and $0.75.
Is DeepSeek-V3.2-Exp or Mercury 2 better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.2-Exp does, with 164K tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Exp and Mercury 2 share?
16 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Mercury 2 has 17.