Model comparison
DeepSeek V3 vs MAI-Thinking-1
Head-to-head evidence from 3 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3 #147 (Supported); MAI-Thinking-1 unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3 and MAI-Thinking-1 share 3 comparable benchmark results. 4 of 8 categories are comparable. 19 results are unique to DeepSeek V3; 10 to MAI-Thinking-1.
Updated July 23, 2026- Shared results
- 3
- DeepSeek V3 only
- 19
- MAI-Thinking-1 only
- 10
- Comparable categories
- 4 / 8
Treat this as a split decision. DeepSeek V3 makes more sense if instruction following is the priority or you would rather avoid the extra latency and token burn of a reasoning model; MAI-Thinking-1 is the better fit if mathematics is the priority or you need the larger 256K context window.
Confidence note. This is a partial-evidence comparison with 3 shared benchmark results across 2 evidence categories; 4 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 and MAI-Thinking-1 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.
MAI-Thinking-1 is the reasoning model in the pair, while DeepSeek V3 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. MAI-Thinking-1 gives you the larger context window at 256K, compared with 128K for DeepSeek V3.
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 | Δ | MAI-Thinking-1 |
|---|---|---|---|
| Math | DeepSeek V31.7 | Margin→ 88.0 | MAI-Thinking-189.7 |
| Coding | DeepSeek V338.9 | Margin→ 26.6 | MAI-Thinking-165.5 |
| Inst. Following | DeepSeek V386.1 | Margin← 1.1 | MAI-Thinking-185.0 |
| Knowledge | DeepSeek V372.7 | Margin← 0.2 | MAI-Thinking-172.5 |
| Agentic | DeepSeek V3Not measured | MarginNo overlap | MAI-Thinking-146.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 42%B 73.5%Winner: MAI-Thinking-1Δ 31.5SWE-bench Verified: DeepSeek V3 scored 42%; MAI-Thinking-1 scored 73.5%. MAI-Thinking-1 wins this benchmark. - Source ↗
GPQA
KnowledgeA 59.1%B 84.2%Winner: MAI-Thinking-1Δ 25.1GPQA: DeepSeek V3 scored 59.1%; MAI-Thinking-1 scored 84.2%. MAI-Thinking-1 wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 75.9%B 85%Winner: MAI-Thinking-1Δ 9.1MMLU-Pro: DeepSeek V3 scored 75.9%; MAI-Thinking-1 scored 85%. MAI-Thinking-1 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3 | MAI-Thinking-1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3$0.27 input / $1.1 output | MAI-Thinking-1Not available | A complete price comparison is not available. |
| Generation speedtokens per second | DeepSeek V3Not available | MAI-Thinking-1Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3Not available | MAI-Thinking-1Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3128K | MAI-Thinking-1256K | MAI-Thinking-1 lists the larger context window. |
Benchmark Deep Dive
Agentic5 benchmarks
CodingMAI-Thinking-1 wins6 benchmarks
Reasoning3 benchmarks
KnowledgeDeepSeek V3 wins10 benchmarks
| Benchmark | DeepSeek V3 | MAI-Thinking-1 | Result |
|---|---|---|---|
| GPQASource | 59.1% | 84.2% | MAI-Thinking-1 leads |
| MMLU-ProSource | 75.9% | 85% | MAI-Thinking-1 leads |
| Artificial Analysis Intelligence IndexSource | 14.2% | — | Not comparable |
| AA-GPQA DiamondSource | 55.7% | — | Not comparable |
| AA-HLESource | 3.6% | — | Not comparable |
| AA-Omniscience IndexSource | -41.3% | — | Not comparable |
| AA-Omniscience AccuracySource | 25.4% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 89.4% | — | Not comparable |
| GPQA-DSource | — | 84.2% | Not comparable |
| SimpleQASource | — | 31% | Not comparable |
MathMAI-Thinking-1 wins4 benchmarks
Multimodal1 benchmarks
| Benchmark | DeepSeek V3 | MAI-Thinking-1 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1150 | — | Not comparable |
Frequently Asked Questions (5)
Which is better, DeepSeek V3 or MAI-Thinking-1?
DeepSeek V3 and MAI-Thinking-1 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 knowledge tasks, DeepSeek V3 or MAI-Thinking-1?
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 72.7 versus 72.5. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, DeepSeek V3 or MAI-Thinking-1?
MAI-Thinking-1 has the edge for coding in this comparison, averaging 65.5 versus 38.9. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Which is better for math, DeepSeek V3 or MAI-Thinking-1?
MAI-Thinking-1 has the edge for math in this comparison, averaging 89.7 versus 1.7. DeepSeek V3 stays close enough that the answer can still flip depending on your workload.
Which is better for instruction following, DeepSeek V3 or MAI-Thinking-1?
DeepSeek V3 has the edge for instruction following in this comparison, averaging 86.1 versus 85. MAI-Thinking-1 stays close enough that the answer can still flip depending on your workload.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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