Model comparison
DeepSeek V3.2 vs DeepSeek V4 Pro (Max)
Head-to-head evidence from 12 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3.2 #82 (Supported); DeepSeek V4 Pro (Max) unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3.2 and DeepSeek V4 Pro (Max) share 12 comparable benchmark results. 2 of 8 categories are comparable. 7 results are unique to DeepSeek V3.2; 36 to DeepSeek V4 Pro (Max).
Updated July 23, 2026- Shared results
- 12
- DeepSeek V3.2 only
- 7
- DeepSeek V4 Pro (Max) only
- 36
- Comparable categories
- 2 / 8
Treat this as a split decision. DeepSeek V3.2 makes more sense if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model; DeepSeek V4 Pro (Max) is the better fit if mathematics is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 6 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
DeepSeek V3.2 and DeepSeek V4 Pro (Max) finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
DeepSeek V4 Pro (Max) is also the more expensive model on tokens at $0.43 input / $0.87 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 2.1x on output cost alone. DeepSeek V4 Pro (Max) is the reasoning model in the pair, while DeepSeek V3.2 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. DeepSeek V4 Pro (Max) gives you the larger context window at 1M, compared with 128K for DeepSeek V3.2.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | DeepSeek V3.2 | Δ | DeepSeek V4 Pro (Max) |
|---|---|---|---|
| Math | DeepSeek V3.217.1 | Margin→ 78.1 | DeepSeek V4 Pro (Max)95.2 |
| Coding | DeepSeek V3.260.9 | Margin→ 10.0 | DeepSeek V4 Pro (Max)70.9 |
| Agentic | DeepSeek V3.2Not measured | MarginNo overlap | DeepSeek V4 Pro (Max)74.5 |
| Knowledge | DeepSeek V3.2Not measured | MarginNo overlap | DeepSeek V4 Pro (Max)60.1 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3.2 | DeepSeek V4 Pro (Max) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3.2$0.28 input / $0.42 output | DeepSeek V4 Pro (Max)$0.435 input / $0.87 output | DeepSeek V3.2 has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3.235 tok/s | DeepSeek V4 Pro (Max)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3.23.75 s | DeepSeek V4 Pro (Max)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3.2128K | DeepSeek V4 Pro (Max)1M | DeepSeek V4 Pro (Max) lists the larger context window. |
Benchmark Deep Dive
Agentic20 benchmarks
| Benchmark | DeepSeek V3.2 | DeepSeek V4 Pro (Max) | Result |
|---|---|---|---|
| Claw-EvalSource | 40.2% | — | Not comparable |
| VITA-BenchSource | 18.5% | — | Not comparable |
| τ²-bench resultsSource | 78.9% | 96.2% | DeepSeek V4 Pro (Max) leads |
| Gert LabsSource | 29.57% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 67.9% | Not comparable |
| BrowseCompSource | — | 83.4% | Not comparable |
| HLE w/ toolsSource | — | 48.2% | Not comparable |
| MCP AtlasSource | — | 73.6% | Not comparable |
| GDPval-AASource | — | 1307 | Not comparable |
| ToolathlonSource | — | 51.8% | Not comparable |
| AA Agentic IndexSource | — | 36.4% | Not comparable |
| APEX-Agents-AASource | — | 24.3% | Not comparable |
| GDPval-AASource | — | 40.4% | Not comparable |
| AA BriefcaseSource | — | 932 | Not comparable |
| AA EnterpriseOps-GymSource | — | 40.4% | Not comparable |
| AA Harvey LABSource | — | 84.4% | Not comparable |
| AA ITBenchSource | — | 38.3% | Not comparable |
| AA Tau3 BankingSource | — | 25.8% | Not comparable |
| terminalBenchHardSource | — | 46.2% | Not comparable |
| aaTerminalBench21Source | — | 64% | Not comparable |
CodingDeepSeek V4 Pro (Max) wins10 benchmarks
| Benchmark | DeepSeek V3.2 | DeepSeek V4 Pro (Max) | Result |
|---|---|---|---|
| SWE-RebenchSource | 60.9% | — | Not comparable |
| React Native EvalsSource | 71.5% | — | Not comparable |
| AA-SciCodeSource | 38.7% | 50.0% | DeepSeek V4 Pro (Max) leads |
| CodeforcesSource | — | 3206.0 | Not comparable |
| SWE-bench VerifiedSource | — | 80.6% | Not comparable |
| SWE-bench ProSource | — | 55.4% | Not comparable |
| SWE MultilingualSource | — | 76.2% | Not comparable |
| Terminal-Bench 2.0Source | — | 67.9% | Not comparable |
| Vibe Code BenchSource | — | 49.93% | Not comparable |
| AA Coding IndexSource | — | 59.4% | Not comparable |
Reasoning4 benchmarks
Knowledge13 benchmarks
| Benchmark | DeepSeek V3.2 | DeepSeek V4 Pro (Max) | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 24.7% | 44.3% | DeepSeek V4 Pro (Max) leads |
| AA-GPQA DiamondSource | 75.1% | 88.8% | DeepSeek V4 Pro (Max) leads |
| AA-HLESource | 10.5% | 35.9% | DeepSeek V4 Pro (Max) leads |
| AA-Omniscience IndexSource | -46.7% | -10.0% | DeepSeek V4 Pro (Max) leads |
| AA-Omniscience AccuracySource | 24.2% | 43.3% | DeepSeek V4 Pro (Max) leads |
| AA-Omniscience Hallucination RateSource | 93.5% | 94.0% | DeepSeek V3.2 leads |
| MMLU-ProSource | — | 87.5% | Not comparable |
| SimpleQASource | — | 57.9% | Not comparable |
| Chinese-SimpleQASource | — | 84.4% | Not comparable |
| GPQASource | — | 90.1% | Not comparable |
| GPQA-DSource | — | 90.1% | Not comparable |
| HLESource | — | 37.7% | Not comparable |
| AA Openness IndexSource | — | 50.0% | Not comparable |
MathDeepSeek V4 Pro (Max) wins6 benchmarks
| Benchmark | DeepSeek V3.2 | DeepSeek V4 Pro (Max) | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | 22.100% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 2.100% | — | Not comparable |
| HMMT Feb 2026Source | — | 95.2% | Not comparable |
| IMOAnswerBenchSource | — | 89.8% | Not comparable |
| ApexSource | — | 38.3% | Not comparable |
| Apex ShortlistSource | — | 90.2% | Not comparable |
Multimodal1 benchmarks
| Benchmark | DeepSeek V3.2 | DeepSeek V4 Pro (Max) | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1204 | 1264 | DeepSeek V4 Pro (Max) leads |
Inst. Following1 benchmarks
| Benchmark | DeepSeek V3.2 | DeepSeek V4 Pro (Max) | Result |
|---|---|---|---|
| AA-IFBenchSource | 49.0% | 76.5% | DeepSeek V4 Pro (Max) leads |
Frequently Asked Questions (3)
Which is better, DeepSeek V3.2 or DeepSeek V4 Pro (Max)?
DeepSeek V3.2 and DeepSeek V4 Pro (Max) are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for coding, DeepSeek V3.2 or DeepSeek V4 Pro (Max)?
DeepSeek V4 Pro (Max) has the edge for coding in this comparison, averaging 70.9 versus 60.9. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for math, DeepSeek V3.2 or DeepSeek V4 Pro (Max)?
DeepSeek V4 Pro (Max) has the edge for math in this comparison, averaging 95.2 versus 17.1. DeepSeek V3.2 stays close enough that the answer can still flip depending on your workload.
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