Model comparison
Amazon Nova Micro vs DeepSeek-V3.1-Terminus
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 30.4 on the Noometry Index. Amazon Nova Micro costs 7.4× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.
Last verified . 10 shared benchmarks.
DeepSeekSummary
- They share 10 benchmarks with published results for both. Amazon Nova Micro scores higher in 0 categories and DeepSeek-V3.1-Terminus in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.0 to 39.5.
- Amazon Nova Micro is cheaper at $0.035 / $0.14 per million input/output tokens, against $0.27 / $1 for DeepSeek-V3.1-Terminus.
- DeepSeek-V3.1-Terminus accepts more context: 164K tokens versus 128K.
- DeepSeek-V3.1-Terminus has downloadable open weights; the other is API-only.
Side by side
| Amazon Nova Micro | DeepSeek-V3.1-Terminus | |
|---|---|---|
| Provider | Amazon | DeepSeek |
| Noometry Index | 30.4 | 43.1 |
| Released | 2024-12-03 | 2025-09-22 |
| Weights | Proprietary | Open |
| Context window | 128K | 164K |
| Max output | 10K | 147K |
| Input $ / M tokens | $0.035 | $0.27 |
| Output $ / M tokens | $0.14 | $1 |
| Results tracked | 32 | 16 |
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Category by category
Coding DeepSeek-V3.1-Terminus leads
Amazon Nova Micro: 30.5 (#295), DeepSeek-V3.1-Terminus: 42.0 (#113)
| Benchmark | Amazon Nova Micro | DeepSeek-V3.1-Terminus |
|---|---|---|
| LMArena Coding | 1218 | 1426 |
| SciCode | — | 40.6% |
| LiveBench Coding | 20.2% | — |
| ALE-Bench | — | 745.17 |
Agentic & Tool Use Not comparable
Amazon Nova Micro: 22.1 (#132), DeepSeek-V3.1-Terminus: —
| Benchmark | Amazon Nova Micro | DeepSeek-V3.1-Terminus |
|---|---|---|
| Berkeley Function Calling Leaderboard | 22.3% | — |
Reasoning DeepSeek-V3.1-Terminus leads
Amazon Nova Micro: 17.4 (#294), DeepSeek-V3.1-Terminus: 26.4 (#133)
| Benchmark | Amazon Nova Micro | DeepSeek-V3.1-Terminus |
|---|---|---|
| LMArena Hard Prompts | 1191 | 1426 |
| Kagi LLM Benchmark | — | 57.4% |
| CritPt | — | 1.7% |
| LiveBench Reasoning | 25.1% | — |
| DTBench | — | 81.3% |
| LiveBench Data Analysis | 34% | — |
| LMCA | — | 28.6% |
| LiveBench | 29.6% | — |
Math DeepSeek-V3.1-Terminus leads
Amazon Nova Micro: 26.9 (#254), DeepSeek-V3.1-Terminus: 38.5 (#137)
| Benchmark | Amazon Nova Micro | DeepSeek-V3.1-Terminus |
|---|---|---|
| LMArena Math | 1206 | 1402 |
| Omni-MATH | 21.4% | — |
| LiveBench Math | 34.5% | — |
Knowledge Not comparable
Amazon Nova Micro: 29.6 (#237), DeepSeek-V3.1-Terminus: —
| Benchmark | Amazon Nova Micro | DeepSeek-V3.1-Terminus |
|---|---|---|
| MMLU-Pro | 51.1% | — |
| Vectara Hallucination Rate | 5.5% | — |
| GPQA (HELM) | 38.3% | — |
| LMArena Expert | 1184 | — |
| MMLU | 70.8% | — |
Multilingual DeepSeek-V3.1-Terminus leads
Amazon Nova Micro: 36.5 (#239), DeepSeek-V3.1-Terminus: 52.1 (#92)
| Benchmark | Amazon Nova Micro | DeepSeek-V3.1-Terminus |
|---|---|---|
| LMArena Non-English | 1186 | 1407 |
| LMArena Russian | 1185 | 1436 |
| LMArena Chinese | 1209 | — |
| LMArena French | 1238 | — |
| LMArena German | 1192 | — |
| LMArena Japanese | 1154 | — |
| LMArena Korean | 1150 | — |
| LMArena Spanish | 1225 | — |
Instruction Following DeepSeek-V3.1-Terminus leads
Amazon Nova Micro: 56.3 (#272), DeepSeek-V3.1-Terminus: 74.0 (#106)
| Benchmark | Amazon Nova Micro | DeepSeek-V3.1-Terminus |
|---|---|---|
| LMArena Instruction Following | 1174 | 1404 |
| LiveBench Instruction Following | 48% | — |
| IFEval | 76% | — |
Long Context DeepSeek-V3.1-Terminus leads
Amazon Nova Micro: 36.5 (#229), DeepSeek-V3.1-Terminus: 43.4 (#97)
| Benchmark | Amazon Nova Micro | DeepSeek-V3.1-Terminus |
|---|---|---|
| LMArena Longer Query | 1205 | 1421 |
Writing & Preference DeepSeek-V3.1-Terminus leads
Amazon Nova Micro: 39.5 (#247), DeepSeek-V3.1-Terminus: 61.0 (#92)
| Benchmark | Amazon Nova Micro | DeepSeek-V3.1-Terminus |
|---|---|---|
| LMArena Text | 1208 | 1419 |
| LMArena Creative Writing | 1172 | 1403 |
| LMArena Multi-Turn | 1178 | 1411 |
| WildBench | 74.3% | — |
| LiveBench Language | 15.8% | — |
Frequently asked questions
Is Amazon Nova Micro better than DeepSeek-V3.1-Terminus?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 30.4 on the Noometry Index. Amazon Nova Micro costs 7.4× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.
Which is cheaper, Amazon Nova Micro or DeepSeek-V3.1-Terminus?
Amazon Nova Micro is cheaper. It lists at $0.035 per million input tokens and $0.14 per million output tokens; DeepSeek-V3.1-Terminus lists at $0.27 and $1.
Is Amazon Nova Micro or DeepSeek-V3.1-Terminus better for coding?
DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 30.5 in the Noometry coding category.
Which has the bigger context window?
DeepSeek-V3.1-Terminus does, with 164K tokens against 128K.
How many benchmarks do Amazon Nova Micro and DeepSeek-V3.1-Terminus share?
10 benchmarks have published results for both models. Amazon Nova Micro has 32 scored results on Noometry and DeepSeek-V3.1-Terminus has 16.