DeepSeek V4 Flash 0423 vs Gemini 2.5 Pro
DeepSeek V4 Flash 0423 leads Gemini 2.5 Pro on the Artificial Analysis Intelligence Index, 24.4 to 16.1, and DeepSeek V4 Flash 0423 costs 35.7x less per output token ($0.28 vs $10.00 per million). Updated October 22, 2026; 44 benchmarks have a result for both.
DeepSeek V4 Flash 0423 or Gemini 2.5 Pro: which to pick
Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; DeepSeek V4 Flash 0423 has none, so the two cannot be ranked on coding.
Pick DeepSeek V4 Flash 0423 for output tokens 35.7x cheaper ($0.28 vs $10.00 per million) and work measured by LMArena WebDev or AA Coding Index, where it leads.
- Shared results
- 44
- DeepSeek V4 Flash 0423 only
- 5
- Gemini 2.5 Pro only
- 46
- Like-for-like categories
- 8 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only Gemini 2.5 Pro has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4 Flash 0423
DeepSeek V4 Flash 0423 leads, 24.4 against 16.1.
Same board, both measured
Agentic work
AA Agentic Index
DeepSeek V4 Flash 0423
DeepSeek V4 Flash 0423 leads, 26.3 against 1.6.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V4 Flash 0423
DeepSeek V4 Flash 0423 leads, 1207 against 1167.
Same board, both measured
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Flash 0423
DeepSeek V4 Flash 0423 $0.0003, Gemini 2.5 Pro $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Gemini 2.5 Pro has the number, $0.64.
Directional only
At a glance
- Coding index
- DeepSeek V4 Flash 0423 not benchmarked, Gemini 2.5 Pro 19.1 from 3 benchmarks.
- Widest category
- Agentic work, 24.7 points apart on average over 1 shared benchmark.
- Widest single gap
- LMArena WebDev: HTML: DeepSeek V4 Flash 0423 1558 against Gemini 2.5 Pro 1235, 53.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Flash 0423 $0.28 against Gemini 2.5 Pro $10.00 per million output tokens, 35.7x apart.
- Context window
- Both take 1.05M tokens.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | DeepSeek V4 Flash 0423 | Bars | Gemini 2.5 Pro | Ties |
|---|---|---|---|---|
| Coding | 4 | 2 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| Web design | 5 | 1 | ||
| General | 2 | 17 | ||
| All shared boards | 14 | 30 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index42
| Benchmark | DeepSeek V4 Flash 0423 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 53.6% | Only Gemini 2.5 Pro measured |
| LMArena WebDev | 1582 | 1226 | DeepSeek V4 Flash 0423 |
| AA Coding Index | 52.0 | 33.3 | DeepSeek V4 Flash 0423 |
| SWE-bench Verified (Vals AI) | Not measured | 54.4% | Only Gemini 2.5 Pro measured |
| Vibe Code Bench (Vals AI) | Not measured | 0.4% | Only Gemini 2.5 Pro measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 80.1% | Only Gemini 2.5 Pro measured |
| SciCode | Not measured | 46.3% | Only Gemini 2.5 Pro measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 26.5% | Only Gemini 2.5 Pro measured |
| Terminal-Bench | Not measured | 32.6% | Only Gemini 2.5 Pro measured |
| GSO-Bench | Not measured | 3.9% | Only Gemini 2.5 Pro measured |
| WeirdML | 45.6% | 54.0% | Gemini 2.5 Pro |
| ALE-Bench | 678.2 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: coding | 1457 | 1452 | DeepSeek V4 Flash 0423 |
| LMArena Text: software and IT | 1453 | 1459 | Gemini 2.5 Pro |
| LMArena WebDev: React | 1585 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena WebDev: HTML | 1558 | 1235 | DeepSeek V4 Flash 0423 |
| LMArena WebDev: from an image | Not measured | 1285 | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Flash 0423 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.4 | 16.1 | DeepSeek V4 Flash 0423 |
| Epoch Capabilities Index | 146.1 | 145.1 | DeepSeek V4 Flash 0423 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Flash 0423 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | 1.6 | DeepSeek V4 Flash 0423 |
| τ²-Bench Telecom | Not measured | 54.1% | Only Gemini 2.5 Pro measured |
| GDPval | Not measured | 23.3% | Only Gemini 2.5 Pro measured |
| DeepResearch Bench | Not measured | 41.5% | Only Gemini 2.5 Pro measured |
| Vending-Bench 2 | Not measured | $574 | Only Gemini 2.5 Pro measured |
| BrowseComp-Plus | Not measured | 29.5% | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | DeepSeek V4 Flash 0423 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 84.4% | Only Gemini 2.5 Pro measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 22.5% | Only Gemini 2.5 Pro measured |
| GPQA Diamond (Epoch AI run) | Not measured | 85.3% | Only Gemini 2.5 Pro measured |
| ARC-AGI | Not measured | 41.0% | Only Gemini 2.5 Pro measured |
| ARC-AGI-2 | Not measured | 4.9% | Only Gemini 2.5 Pro measured |
| SimpleBench | 46.3% | Not measured | Only DeepSeek V4 Flash 0423 measured |
| LMArena Text: hard prompts | 1444 | 1455 | Gemini 2.5 Pro |
| LMArena Text: expert | 1441 | 1452 | Gemini 2.5 Pro |
| LMArena Text: hard prompts in English | 1449 | 1453 | Gemini 2.5 Pro |
| LMArena Vision: diagrams | Not measured | 1274 | Only Gemini 2.5 Pro measured |
| LMArena Vision: homework | Not measured | 1281 | Only Gemini 2.5 Pro measured |
| Kagi LLM Benchmark | 52.2% | 70.3% | Gemini 2.5 Pro |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V4 Flash 0423 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 87.7 | Only Gemini 2.5 Pro measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 87.7% | Only Gemini 2.5 Pro measured |
| OTIS Mock AIME | Not measured | 84.7% | Only Gemini 2.5 Pro measured |
| FrontierMath, tiers 1 to 3 | Not measured | 24.6% | Only Gemini 2.5 Pro measured |
| FrontierMath, tier 4 | Not measured | 0.0% | Only Gemini 2.5 Pro measured |
| LMArena Text: math | 1426 | 1450 | Gemini 2.5 Pro |
| LMArena Text: mathematical work | 1432 | 1454 | Gemini 2.5 Pro |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | DeepSeek V4 Flash 0423 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | Not measured | 63.9% | Only Gemini 2.5 Pro measured |
| CorpFin (Vals AI) | Not measured | 60.8% | Only Gemini 2.5 Pro measured |
| MortgageTax (Vals AI) | Not measured | 68.9% | Only Gemini 2.5 Pro measured |
| MedCode (Vals AI) | Not measured | 50.6% | Only Gemini 2.5 Pro measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 86.2% | Only Gemini 2.5 Pro measured |
| LMArena Text: science | 1446 | 1480 | Gemini 2.5 Pro |
| LMArena Text: medicine and healthcare | 1448 | 1476 | Gemini 2.5 Pro |
| LMArena Text: legal and government | 1439 | 1480 | Gemini 2.5 Pro |
| LMArena Text: business and finance | 1425 | 1439 | Gemini 2.5 Pro |
| FACTS Benchmark Suite | Not measured | 61.5% | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index51
| Benchmark | DeepSeek V4 Flash 0423 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1207 | 1167 | DeepSeek V4 Flash 0423 |
| Design Arena: UI components | 1162 | 1137 | DeepSeek V4 Flash 0423 |
| Design Arena: game dev | 1196 | 1111 | DeepSeek V4 Flash 0423 |
| Design Arena: data viz | 1127 | 1222 | Gemini 2.5 Pro |
| Design Arena: 3D | 1188 | 1080 | DeepSeek V4 Flash 0423 |
| Design Arena: all code categories | 1205 | 1152 | DeepSeek V4 Flash 0423 |
| Design Arena: SVG | 1155 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Design Arena: ASCII art | 1109 | Not measured | Only DeepSeek V4 Flash 0423 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index217
| Benchmark | DeepSeek V4 Flash 0423 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 41.9% | Only Gemini 2.5 Pro measured |
| MedScribe (Vals AI) | Not measured | 73.5% | Only Gemini 2.5 Pro measured |
| IFBench | Not measured | 48.7% | Only Gemini 2.5 Pro measured |
| Long context reasoning (AA-LCR) | Not measured | 69.0% | Only Gemini 2.5 Pro measured |
| DTBench | 86.4% | 82.4% | DeepSeek V4 Flash 0423 |
| LMCA | 35.9% | 34.8% | DeepSeek V4 Flash 0423 |
| Creative writing (Lech Mazur) | Not measured | 83.8% | Only Gemini 2.5 Pro measured |
| LMArena Text | 1432 | 1458 | Gemini 2.5 Pro |
| LMArena Text: instruction following | 1421 | 1437 | Gemini 2.5 Pro |
| LMArena Text: creative writing | 1403 | 1454 | Gemini 2.5 Pro |
| LMArena Vision | Not measured | 1263 | Only Gemini 2.5 Pro measured |
| LMArena Text: English | 1441 | 1458 | Gemini 2.5 Pro |
| LMArena Text: non-English | 1419 | 1452 | Gemini 2.5 Pro |
| LMArena Text: Chinese | 1463 | 1507 | Gemini 2.5 Pro |
| LMArena Text: German | 1419 | 1487 | Gemini 2.5 Pro |
| LMArena Text: Japanese | 1389 | 1461 | Gemini 2.5 Pro |
| LMArena Text: Korean | 1383 | 1434 | Gemini 2.5 Pro |
| LMArena Text: French | 1439 | 1473 | Gemini 2.5 Pro |
| LMArena Text: Spanish | 1436 | 1474 | Gemini 2.5 Pro |
| LMArena Text: Russian | 1427 | 1461 | Gemini 2.5 Pro |
| LMArena Text: Polish | 1436 | 1479 | Gemini 2.5 Pro |
| LMArena Text: multi-turn | 1449 | 1453 | Gemini 2.5 Pro |
| LMArena Text: longer queries | 1434 | 1449 | Gemini 2.5 Pro |
| LMArena Text: writing and language | 1410 | 1454 | Gemini 2.5 Pro |
| LMArena Text: entertainment and media | 1400 | 1441 | Gemini 2.5 Pro |
| LMArena Vision: English | Not measured | 1261 | Only Gemini 2.5 Pro measured |
| LMArena Vision: Chinese | Not measured | 1300 | Only Gemini 2.5 Pro measured |
| LMArena Vision: reading text | Not measured | 1270 | Only Gemini 2.5 Pro measured |
| LMArena Vision: humor | Not measured | 1262 | Only Gemini 2.5 Pro measured |
| LMArena Vision: captioning | Not measured | 1304 | Only Gemini 2.5 Pro measured |
| LMArena Vision: recognizing things | Not measured | 1283 | Only Gemini 2.5 Pro measured |
| LMArena Vision: creative writing | Not measured | 1265 | Only Gemini 2.5 Pro measured |
| LMArena Document | Not measured | 1421 | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | Gemini 2.5 Pro | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
DeepSeek V4 Flash 0423 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Flash 0423 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Flash 0423 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | DeepSeek V4 Flash 0423 | Google Gemini 2.5 Pro |
|---|---|---|
| Maker | DeepSeek | |
| Weights | Open weights | No public weights listed |
| Released | April 24, 2026 | June 17, 2025 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.14 | $1.25 |
| Output $/Mtok | $0.28 | $10.00 |
| Coding index | Not benchmarked | 19.1 |
| Independent measurements behind it | 1 | 3 |
| Cost per solved task | Not computable | $0.64 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Flash 0423 better than Gemini 2.5 Pro for coding?
Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; DeepSeek V4 Flash 0423 has none, so the two cannot be ranked on coding. Pick DeepSeek V4 Flash 0423 for output tokens 35.7x cheaper ($0.28 vs $10.00 per million) and work measured by LMArena WebDev or AA Coding Index, where it leads.
Which is cheaper, DeepSeek V4 Flash 0423 or Gemini 2.5 Pro?
DeepSeek V4 Flash 0423 costs $0.14 per million input tokens and $0.28 per million output tokens; Gemini 2.5 Pro costs $1.25 and $10.00. Cost per solved task is known for Gemini 2.5 Pro only.
Which has the bigger context window, DeepSeek V4 Flash 0423 or Gemini 2.5 Pro?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4 Flash 0423 vs Gemini 2.5 Pro comparison based on?
44 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Flash 0423's coding index rests on 1 independent benchmark and Gemini 2.5 Pro's on 3. Where only one model has a result, the page says so instead of naming a winner.