Model comparison
DeepSeek V3.2 (Thinking) vs o3
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3.2 (Thinking) #65 (Estimated); o3 #131 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3.2 (Thinking) and o3 share 1 comparable benchmark result. 0 of 8 categories are comparable. 1 result is unique to DeepSeek V3.2 (Thinking); 15 to o3.
Updated July 20, 2026- Shared results
- 1
- DeepSeek V3.2 (Thinking) only
- 1
- o3 only
- 15
- Comparable categories
- 0 / 8
Benchmark data for DeepSeek V3.2 (Thinking) and o3 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
o3 is priced at $2.00 input / $8.00 output per 1M tokens, versus $0.55 input / $2.19 output per 1M tokens for DeepSeek V3.2 (Thinking). o3 has the larger context window at 200K, compared with 128K for DeepSeek V3.2 (Thinking).
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 (Thinking) | Δ | o3 |
|---|---|---|---|
| Math | DeepSeek V3.2 (Thinking)Not measured | MarginNo overlap | o314.5 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3.2 (Thinking) | o3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3.2 (Thinking)$0.55 input / $2.19 output | o3$2 input / $8 output | DeepSeek V3.2 (Thinking) has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3.2 (Thinking)Not available | o3118 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3.2 (Thinking)Not available | o35.38 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3.2 (Thinking)128K | o3200K | o3 lists the larger context window. |
Benchmark Deep Dive
Agentic1 benchmarks
| Benchmark | DeepSeek V3.2 (Thinking) | o3 | Result |
|---|---|---|---|
| τ²-bench resultsSource | — | 80.7% | Not comparable |
Coding2 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | DeepSeek V3.2 (Thinking) | o3 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | — | 30.4% | Not comparable |
| AA-GPQA DiamondSource | — | 82.7% | Not comparable |
| AA-HLESource | — | 20.0% | Not comparable |
| AA-Omniscience IndexSource | — | -15.3% | Not comparable |
| AA-Omniscience AccuracySource | — | 38.4% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 87.1% | Not comparable |
Math3 benchmarks
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | DeepSeek V3.2 (Thinking) | o3 | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 71.4% | Not comparable |
Frequently Asked Questions (3)
Can I compare DeepSeek V3.2 (Thinking) and o3 on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for DeepSeek V3.2 (Thinking) and o3 today?
DeepSeek V3.2 (Thinking): $0.55 input / $2.19 output per 1M tokens o3: $2.00 input / $8.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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