Model comparison
DeepSeek V4 Pro vs Mercury 2.5
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 33.5 on the Noometry Index. Mercury 2.5 costs 15× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.
Last verified . 4 shared benchmarks.
DeepSeekSummary
- They share 4 benchmarks with published results for both. DeepSeek V4 Pro scores higher in 3 categories and Mercury 2.5 in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek V4 Pro leads 64.8 to 23.3.
- The biggest single-benchmark swing is ProofBench: 50% for DeepSeek V4 Pro and 3% for Mercury 2.5.
- Mercury 2.5 is cheaper at $0.04 / $0.15 per million input/output tokens, against $0.66 / $1.98 for DeepSeek V4 Pro.
- DeepSeek V4 Pro accepts more context: 1M tokens versus 260K.
- DeepSeek V4 Pro has downloadable open weights; the other is API-only.
Side by side
| DeepSeek V4 Pro | Mercury 2.5 | |
|---|---|---|
| Provider | DeepSeek | Inception |
| Noometry Index | 54.3 | 33.5 |
| Released | 2026-04-24 | 2026-09-08 |
| Weights | Open | Proprietary |
| Context window | 1M | 260K |
| Max output | 393K | 66K |
| Input $ / M tokens | $0.66 | $0.04 |
| Output $ / M tokens | $1.98 | $0.15 |
| Results tracked | 48 | 4 |
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Category by category
Coding DeepSeek V4 Pro leads
DeepSeek V4 Pro: 52.4 (#34), Mercury 2.5: 39.5 (#156)
| Benchmark | DeepSeek V4 Pro | Mercury 2.5 |
|---|---|---|
| SciCode | 51% | 38.5% |
| ALE-Bench | 1,403 | 301.65 |
| SWE-bench Verified | 77.6% | — |
| FrontierCode | 28.6% | — |
| LMArena WebDev | 1582 | — |
| WeirdML | 66.2% | — |
| LMArena Coding | 1470 | — |
Agentic & Tool Use Not comparable
DeepSeek V4 Pro: 32.8 (#58), Mercury 2.5: —
| Benchmark | DeepSeek V4 Pro | Mercury 2.5 |
|---|---|---|
| APEX-Agents | 47.3% | — |
| Vending-Bench 2 | 3,285 | — |
Reasoning DeepSeek V4 Pro leads
DeepSeek V4 Pro: 56.5 (#24), Mercury 2.5: 22.4 (#193)
| Benchmark | DeepSeek V4 Pro | Mercury 2.5 |
|---|---|---|
| CritPt | 18% | 0% |
| ARC-AGI-2 | 61.3% | — |
| Kagi LLM Benchmark | 53.5% | — |
| NYT Connections (extended) | 91.3% | — |
| ARC-AGI-1 | 90.5% | — |
| Chess Puzzles | 47% | — |
| LMArena Hard Prompts | 1461 | — |
| Mystery Game Puzzles | 43% | — |
| DTBench | 93.9% | — |
| LMCA | 45.5% | — |
| Surface Evolver Bench | 40% | — |
| Epoch Capabilities Index | 155.31 | — |
| ForecastBench | 56.1 | — |
Math DeepSeek V4 Pro leads
DeepSeek V4 Pro: 64.8 (#30), Mercury 2.5: 23.3 (#272)
| Benchmark | DeepSeek V4 Pro | Mercury 2.5 |
|---|---|---|
| ProofBench | 50% | 3% |
| FrontierMath (Tiers 1-3) | 64.6% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 76.6% | — |
| OTIS Mock AIME 2024-2025 | 98.6% | — |
| LMArena Math | 1455 | — |
Knowledge Not comparable
DeepSeek V4 Pro: 59.5 (#31), Mercury 2.5: —
| Benchmark | DeepSeek V4 Pro | Mercury 2.5 |
|---|---|---|
| GPQA Diamond | 91.7% | — |
| SimpleQA Verified | 52.9% | — |
| Vectara Hallucination Rate | 8.6% | — |
| LMArena Expert | 1464 | — |
Multilingual Not comparable
DeepSeek V4 Pro: 54.4 (#45), Mercury 2.5: —
| Benchmark | DeepSeek V4 Pro | Mercury 2.5 |
|---|---|---|
| LMArena Non-English | 1439 | — |
| LMArena Chinese | 1486 | — |
| LMArena French | 1472 | — |
| LMArena German | 1458 | — |
| LMArena Japanese | 1445 | — |
| LMArena Korean | 1447 | — |
| LMArena Russian | 1453 | — |
| LMArena Spanish | 1458 | — |
Instruction Following Not comparable
DeepSeek V4 Pro: 76.1 (#47), Mercury 2.5: —
| Benchmark | DeepSeek V4 Pro | Mercury 2.5 |
|---|---|---|
| LMArena Instruction Following | 1448 | — |
Long Context Not comparable
DeepSeek V4 Pro: 45.0 (#51), Mercury 2.5: —
| Benchmark | DeepSeek V4 Pro | Mercury 2.5 |
|---|---|---|
| CL-bench Life | 13.5% | — |
| LMArena Longer Query | 1458 | — |
Writing & Preference Not comparable
DeepSeek V4 Pro: 65.5 (#46), Mercury 2.5: —
| Benchmark | DeepSeek V4 Pro | Mercury 2.5 |
|---|---|---|
| LMArena Text | 1451 | — |
| LMArena Creative Writing | 1446 | — |
| EQ-Bench Creative Writing | 1553 | — |
| EQ-Bench 4 | 1166 | — |
| LMArena Multi-Turn | 1467 | — |
Frequently asked questions
Is DeepSeek V4 Pro better than Mercury 2.5?
DeepSeek V4 Pro is the stronger model overall, scoring 54.3 to 33.5 on the Noometry Index. Mercury 2.5 costs 15× less per token, which makes it the better buy when DeepSeek V4 Pro's lead doesn't matter for your workload.
Which is cheaper, DeepSeek V4 Pro 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 V4 Pro lists at $0.66 and $1.98.
Is DeepSeek V4 Pro or Mercury 2.5 better for coding?
DeepSeek V4 Pro scores higher on coding benchmarks: 52.4 versus 39.5 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4 Pro does, with 1M tokens against 260K.
How many benchmarks do DeepSeek V4 Pro and Mercury 2.5 share?
4 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and Mercury 2.5 has 4.