Model comparison
DeepSeek V3 vs Gemini 3.5 Flash-Lite
Head-to-head evidence from 13 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3 #147 (Supported); Gemini 3.5 Flash-Lite unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3 and Gemini 3.5 Flash-Lite share 13 comparable benchmark results. 1 of 8 categories are comparable. 9 results are unique to DeepSeek V3; 8 to Gemini 3.5 Flash-Lite.
Updated July 21, 2026- Shared results
- 13
- DeepSeek V3 only
- 9
- Gemini 3.5 Flash-Lite only
- 8
- Comparable categories
- 1 / 8
Treat this as a split decision. DeepSeek V3 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; Gemini 3.5 Flash-Lite is the better fit if coding is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 13 shared benchmark results across 4 evidence categories; 1 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 Gemini 3.5 Flash-Lite 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.
Gemini 3.5 Flash-Lite is also the more expensive model on tokens at $0.30 input / $2.50 output per 1M tokens, versus $0.27 input / $1.10 output per 1M tokens for DeepSeek V3. That is roughly 2.3x on output cost alone. Gemini 3.5 Flash-Lite 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. Gemini 3.5 Flash-Lite gives you the larger context window at 1M, 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 | Gemini 3.5 Flash-Lite |
|---|---|---|---|
| Coding | DeepSeek V338.9 | Margin→ 15.3 | Gemini 3.5 Flash-Lite54.2 |
| Agentic | DeepSeek V3Not measured | MarginNo overlap | Gemini 3.5 Flash-Lite63.4 |
| Reasoning | DeepSeek V3Not measured | MarginNo overlap | Gemini 3.5 Flash-Lite72.2 |
| Knowledge | DeepSeek V372.7 | MarginNo overlap | Gemini 3.5 Flash-LiteNot measured |
| Math | DeepSeek V31.7 | MarginNo overlap | Gemini 3.5 Flash-LiteNot measured |
| Inst. Following | DeepSeek V386.1 | MarginNo overlap | Gemini 3.5 Flash-LiteNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3 | Gemini 3.5 Flash-Lite | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3$0.27 input / $1.1 output | Gemini 3.5 Flash-Lite$0.3 input / $2.5 output | DeepSeek V3 has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3Not available | Gemini 3.5 Flash-LiteNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3Not available | Gemini 3.5 Flash-LiteNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3128K | Gemini 3.5 Flash-Lite1M | Gemini 3.5 Flash-Lite lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | DeepSeek V3 | Gemini 3.5 Flash-Lite | Result |
|---|---|---|---|
| AA Agentic IndexSource | 1.6% | 26.8% | Gemini 3.5 Flash-Lite leads |
| τ²-bench resultsSource | 22.8% | — | Not comparable |
| GDPval-AASource | 0.0% | 32.0% | Gemini 3.5 Flash-Lite leads |
| GDPval-AASource | 217 | 1140 | Gemini 3.5 Flash-Lite leads |
| Terminal-Bench 2.0Source | — | 54% | Not comparable |
| OSWorld-VerifiedSource | — | 74% | Not comparable |
| AA BriefcaseSource | — | 634 | Not comparable |
| AA Tau3 BankingSource | — | 16.5% | Not comparable |
CodingGemini 3.5 Flash-Lite wins6 benchmarks
| Benchmark | DeepSeek V3 | Gemini 3.5 Flash-Lite | Result |
|---|---|---|---|
| LiveCodeBenchSource | 37.6% | — | Not comparable |
| SWE-bench VerifiedSource | 42% | — | Not comparable |
| AA Coding IndexSource | 23.0% | 49.3% | Gemini 3.5 Flash-Lite leads |
| AA-SciCodeSource | 35.4% | 40.9% | Gemini 3.5 Flash-Lite leads |
| Terminal-Bench 2.0Source | — | 54.0% | Not comparable |
| SWE-bench ProSource | — | 54.2% | Not comparable |
Reasoning3 benchmarks
Knowledge8 benchmarks
| Benchmark | DeepSeek V3 | Gemini 3.5 Flash-Lite | Result |
|---|---|---|---|
| GPQASource | 59.1% | — | Not comparable |
| MMLU-ProSource | 75.9% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 14.2% | 36.5% | Gemini 3.5 Flash-Lite leads |
| AA-GPQA DiamondSource | 55.7% | 83.8% | Gemini 3.5 Flash-Lite leads |
| AA-HLESource | 3.6% | 17.5% | Gemini 3.5 Flash-Lite leads |
| AA-Omniscience IndexSource | -41.3% | 6.9% | Gemini 3.5 Flash-Lite leads |
| AA-Omniscience AccuracySource | 25.4% | 30.3% | Gemini 3.5 Flash-Lite leads |
| AA-Omniscience Hallucination RateSource | 89.4% | 33.5% | Gemini 3.5 Flash-Lite leads |
Math1 benchmarks
| Benchmark | DeepSeek V3 | Gemini 3.5 Flash-Lite | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | 1.724% | — | Not comparable |
Multimodal2 benchmarks
Frequently Asked Questions (2)
Which is better, DeepSeek V3 or Gemini 3.5 Flash-Lite?
DeepSeek V3 and Gemini 3.5 Flash-Lite 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 or Gemini 3.5 Flash-Lite?
Gemini 3.5 Flash-Lite has the edge for coding in this comparison, averaging 54.2 versus 38.9. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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