# DeepSeek-V3.2-Exp vs Step 3.7 Flash

> DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 37.3 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-exp-vs-step-3-7-flash
- Last updated: 2026-10-10
- Shared benchmarks: 4

## Summary

- They share 4 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 2 categories and Step 3.7 Flash in 1 category; 2 gaps are clear of the uncertainty.
- The widest gap is in coding, where DeepSeek-V3.2-Exp leads 46.5 to 40.0.
- The biggest single-benchmark swing is MathArena Final-Answer Competitions: 57.7% for DeepSeek-V3.2-Exp and 68.5% for Step 3.7 Flash.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $0.18 / $1.11 for Step 3.7 Flash.
- Step 3.7 Flash accepts more context: 256K tokens versus 164K.

## Snapshot

| | DeepSeek-V3.2-Exp | Step 3.7 Flash |
|---|---|---|
| Provider | DeepSeek | StepFun |
| Noometry Index | 44.3 | 37.3 |
| Rank | 78 | 207 |
| Context | 164K | 256K |
| Input $/M | $0.26 | $0.18 |
| Output $/M | $0.38 | $1.11 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.2-Exp: 46.5 (#65)
- Step 3.7 Flash: 40.0 (#150)

| Benchmark | DeepSeek-V3.2-Exp | Step 3.7 Flash |
|---|---|---|
| SciCode | 38.9% | 40% |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| WeirdML | 39.5% | — |
| LMArena Coding | 1454 | — |
| ALE-Bench | — | 694.12 |

## Agentic & Tool Use

- DeepSeek-V3.2-Exp: 32.7 (#59)
- Step 3.7 Flash: —

| Benchmark | DeepSeek-V3.2-Exp | Step 3.7 Flash |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| APEX-Agents | 21.3% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |

## Reasoning

- DeepSeek-V3.2-Exp: 22.1 (#208)
- Step 3.7 Flash: 21.6 (#219)

| Benchmark | DeepSeek-V3.2-Exp | Step 3.7 Flash |
|---|---|---|
| NYT Connections (extended) | 36.7% | 39.7% |
| CritPt | 2.9% | 2.3% |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| ARC-AGI-1 | 57% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| LMArena Hard Prompts | 1434 | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.27 | — |

## Math

- DeepSeek-V3.2-Exp: 41.7 (#87)
- Step 3.7 Flash: 42.9 (#82)

| Benchmark | DeepSeek-V3.2-Exp | Step 3.7 Flash |
|---|---|---|
| MathArena Final-Answer Competitions | 57.7% | 68.5% |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| LMArena Math | 1435 | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- DeepSeek-V3.2-Exp: 51.7 (#66)
- Step 3.7 Flash: —

| Benchmark | DeepSeek-V3.2-Exp | Step 3.7 Flash |
|---|---|---|
| GPQA Diamond | 83.4% | — |
| Vectara Hallucination Rate | 5.3% | — |
| LMArena Expert | 1436 | — |

## Multilingual

- DeepSeek-V3.2-Exp: 52.2 (#90)
- Step 3.7 Flash: —

| Benchmark | DeepSeek-V3.2-Exp | Step 3.7 Flash |
|---|---|---|
| LMArena Non-English | 1409 | — |
| LMArena Chinese | 1461 | — |
| LMArena French | 1433 | — |
| LMArena German | 1440 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1371 | — |
| LMArena Russian | 1424 | — |
| LMArena Spanish | 1440 | — |

## Instruction Following

- DeepSeek-V3.2-Exp: 74.5 (#93)
- Step 3.7 Flash: —

| Benchmark | DeepSeek-V3.2-Exp | Step 3.7 Flash |
|---|---|---|
| LMArena Instruction Following | 1413 | — |

## Long Context

- DeepSeek-V3.2-Exp: 47.6 (#16)
- Step 3.7 Flash: —

| Benchmark | DeepSeek-V3.2-Exp | Step 3.7 Flash |
|---|---|---|
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
| LMArena Longer Query | 1428 | — |

## Writing & Preference

- DeepSeek-V3.2-Exp: 62.4 (#77)
- Step 3.7 Flash: —

| Benchmark | DeepSeek-V3.2-Exp | Step 3.7 Flash |
|---|---|---|
| LMArena Text | 1425 | — |
| LMArena Creative Writing | 1403 | — |
| EQ-Bench Creative Writing | 1515 | — |
| LMArena Multi-Turn | 1427 | — |

## FAQ

### Is DeepSeek-V3.2-Exp better than Step 3.7 Flash?

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 37.3 on the Noometry Index.

### Which is cheaper, DeepSeek-V3.2-Exp or Step 3.7 Flash?

DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; Step 3.7 Flash lists at $0.18 and $1.11.

### Is DeepSeek-V3.2-Exp or Step 3.7 Flash better for coding?

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 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-V3.2-Exp and Step 3.7 Flash share?

4 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Step 3.7 Flash has 5.