Model comparison
DeepSeek-V3.1-Terminus vs Step 3.7 Flash
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 37.3 on the Noometry Index.
Last verified . 3 shared benchmarks.
DeepSeek
StepFunSummary
- They share 3 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 2 categories and Step 3.7 Flash in 1 category; 3 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek-V3.1-Terminus leads 26.4 to 21.6.
- Step 3.7 Flash is cheaper at $0.18 / $1.11 per million input/output tokens, against $0.27 / $1 for DeepSeek-V3.1-Terminus.
- Step 3.7 Flash accepts more context: 256K tokens versus 164K.
Side by side
| DeepSeek-V3.1-Terminus | Step 3.7 Flash | |
|---|---|---|
| Provider | DeepSeek | StepFun |
| Noometry Index | 43.1 | 37.3 |
| Released | 2025-09-22 | 2026-05-29 |
| Weights | Open | Open |
| Context window | 164K | 256K |
| Max output | 147K | 256K |
| Input $ / M tokens | $0.27 | $0.18 |
| Output $ / M tokens | $1 | $1.11 |
| Results tracked | 16 | 5 |
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Category by category
Coding DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 42.0 (#113), Step 3.7 Flash: 40.0 (#150)
| Benchmark | DeepSeek-V3.1-Terminus | Step 3.7 Flash |
|---|---|---|
| SciCode | 40.6% | 40% |
| ALE-Bench | 745.17 | 694.12 |
| LMArena Coding | 1426 | — |
Reasoning DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 26.4 (#133), Step 3.7 Flash: 21.6 (#219)
| Benchmark | DeepSeek-V3.1-Terminus | Step 3.7 Flash |
|---|---|---|
| CritPt | 1.7% | 2.3% |
| Kagi LLM Benchmark | 57.4% | — |
| NYT Connections (extended) | — | 39.7% |
| LMArena Hard Prompts | 1426 | — |
| DTBench | 81.3% | — |
| LMCA | 28.6% | — |
Math Step 3.7 Flash leads
DeepSeek-V3.1-Terminus: 38.5 (#137), Step 3.7 Flash: 42.9 (#82)
| Benchmark | DeepSeek-V3.1-Terminus | Step 3.7 Flash |
|---|---|---|
| MathArena Final-Answer Competitions | — | 68.5% |
| LMArena Math | 1402 | — |
Multilingual Not comparable
DeepSeek-V3.1-Terminus: 52.1 (#92), Step 3.7 Flash: —
| Benchmark | DeepSeek-V3.1-Terminus | Step 3.7 Flash |
|---|---|---|
| LMArena Non-English | 1407 | — |
| LMArena Russian | 1436 | — |
Instruction Following Not comparable
DeepSeek-V3.1-Terminus: 74.0 (#106), Step 3.7 Flash: —
| Benchmark | DeepSeek-V3.1-Terminus | Step 3.7 Flash |
|---|---|---|
| LMArena Instruction Following | 1404 | — |
Long Context Not comparable
DeepSeek-V3.1-Terminus: 43.4 (#97), Step 3.7 Flash: —
| Benchmark | DeepSeek-V3.1-Terminus | Step 3.7 Flash |
|---|---|---|
| LMArena Longer Query | 1421 | — |
Writing & Preference Not comparable
DeepSeek-V3.1-Terminus: 61.0 (#92), Step 3.7 Flash: —
| Benchmark | DeepSeek-V3.1-Terminus | Step 3.7 Flash |
|---|---|---|
| LMArena Text | 1419 | — |
| LMArena Creative Writing | 1403 | — |
| LMArena Multi-Turn | 1411 | — |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than Step 3.7 Flash?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 37.3 on the Noometry Index.
Which is cheaper, DeepSeek-V3.1-Terminus or Step 3.7 Flash?
Step 3.7 Flash is cheaper. It lists at $0.18 per million input tokens and $1.11 per million output tokens; DeepSeek-V3.1-Terminus lists at $0.27 and $1.
Is DeepSeek-V3.1-Terminus or Step 3.7 Flash better for coding?
DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 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-V3.1-Terminus and Step 3.7 Flash share?
3 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Step 3.7 Flash has 5.