Model comparison
DeepSeek-V3 vs Step 3.7 Flash
DeepSeek-V3 is the stronger model overall, scoring 39.5 to 37.3 on the Noometry Index.
Last verified . 2 shared benchmarks.
DeepSeek
StepFunSummary
- They share 2 benchmarks with published results for both. DeepSeek-V3 scores higher in 1 category and Step 3.7 Flash in 2 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where Step 3.7 Flash leads 42.9 to 32.1.
- Both cost about the same: $0.24 input and $0.90 output per million tokens.
- Step 3.7 Flash accepts more context: 256K tokens versus 164K.
Side by side
| DeepSeek-V3 | Step 3.7 Flash | |
|---|---|---|
| Provider | DeepSeek | StepFun |
| Noometry Index | 39.5 | 37.3 |
| Released | 2024-12-26 | 2026-05-29 |
| Weights | Open | Open |
| Context window | 164K | 256K |
| Max output | 164K | 256K |
| Input $ / M tokens | $0.24 | $0.18 |
| Output $ / M tokens | $0.90 | $1.11 |
| Results tracked | 60 | 5 |
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Category by category
Coding DeepSeek-V3 leads
DeepSeek-V3: 42.3 (#106), Step 3.7 Flash: 40.0 (#150)
| Benchmark | DeepSeek-V3 | Step 3.7 Flash |
|---|---|---|
| SciCode | 35.8% | 40% |
| Aider Polyglot | 55.1% | — |
| WeirdML | 36.1% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| LMArena Coding | 1368 | — |
| BigCodeBench Complete | 62.2% | — |
| ALE-Bench | — | 694.12 |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, Step 3.7 Flash: —
| Benchmark | DeepSeek-V3 | Step 3.7 Flash |
|---|---|---|
| METR Time Horizons | 49.6% | — |
Reasoning Step 3.7 Flash leads
DeepSeek-V3: 20.5 (#236), Step 3.7 Flash: 21.6 (#219)
| Benchmark | DeepSeek-V3 | Step 3.7 Flash |
|---|---|---|
| CritPt | 0% | 2.3% |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| NYT Connections (extended) | — | 39.7% |
| LiveBench Reasoning | 65.8% | — |
| LMArena Hard Prompts | 1365 | — |
| DTBench | 64.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| LMCA | 15.5% | — |
| BIG-Bench Hard | 87.5% | — |
| Epoch Capabilities Index | 135.94 | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |
Math Step 3.7 Flash leads
DeepSeek-V3: 32.1 (#219), Step 3.7 Flash: 42.9 (#82)
| Benchmark | DeepSeek-V3 | Step 3.7 Flash |
|---|---|---|
| MathArena Final-Answer Competitions | — | 68.5% |
| OTIS Mock AIME 2024-2025 | 37.8% | — |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| LMArena Math | 1373 | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge Not comparable
DeepSeek-V3: 37.5 (#155), Step 3.7 Flash: —
| Benchmark | DeepSeek-V3 | Step 3.7 Flash |
|---|---|---|
| GPQA Diamond | 67.6% | — |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| Vectara Hallucination Rate | 6.1% | — |
| GPQA (HELM) | 53.8% | — |
| LMArena Expert | 1351 | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |
Multilingual Not comparable
DeepSeek-V3: 48.5 (#143), Step 3.7 Flash: —
| Benchmark | DeepSeek-V3 | Step 3.7 Flash |
|---|---|---|
| LMArena Non-English | 1358 | — |
| LMArena Chinese | 1391 | — |
| LMArena French | 1385 | — |
| LMArena German | 1374 | — |
| LMArena Japanese | 1333 | — |
| LMArena Korean | 1319 | — |
| LMArena Russian | 1373 | — |
| LMArena Spanish | 1358 | — |
Instruction Following Not comparable
DeepSeek-V3: 72.8 (#130), Step 3.7 Flash: —
| Benchmark | DeepSeek-V3 | Step 3.7 Flash |
|---|---|---|
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |
| LMArena Instruction Following | 1345 | — |
Long Context Not comparable
DeepSeek-V3: 34.0 (#253), Step 3.7 Flash: —
| Benchmark | DeepSeek-V3 | Step 3.7 Flash |
|---|---|---|
| Fiction.LiveBench | 50% | — |
| LMArena Longer Query | 1352 | — |
Writing & Preference Not comparable
DeepSeek-V3: 57.4 (#130), Step 3.7 Flash: —
| Benchmark | DeepSeek-V3 | Step 3.7 Flash |
|---|---|---|
| LMArena Text | 1375 | — |
| LMArena Creative Writing | 1364 | — |
| Short-Story Creative Writing | 77% | — |
| EQ-Bench Creative Writing | 1472 | — |
| WildBench | 83% | — |
| LMArena Multi-Turn | 1389 | — |
| LiveBench Language | 49.1% | — |
Frequently asked questions
Is DeepSeek-V3 better than Step 3.7 Flash?
DeepSeek-V3 is the stronger model overall, scoring 39.5 to 37.3 on the Noometry Index.
Which is cheaper, DeepSeek-V3 or Step 3.7 Flash?
DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; Step 3.7 Flash lists at $0.18 and $1.11.
Is DeepSeek-V3 or Step 3.7 Flash better for coding?
DeepSeek-V3 scores higher on coding benchmarks: 42.3 versus 40.0 in the Noometry coding category.
Which has the bigger context window?
Step 3.7 Flash does, with 256K tokens against 164K.
How many benchmarks do DeepSeek-V3 and Step 3.7 Flash share?
2 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Step 3.7 Flash has 5.