Model comparison
DeepSeek V4.1 Flash vs Llama 3-8B
DeepSeek V4.1 Flash is the stronger model overall, scoring 50.3 to 25.3 on the Noometry Index. Llama 3-8B costs 1.9× less per token, which makes it the better buy when DeepSeek V4.1 Flash's lead doesn't matter for your workload.
Last verified . 16 shared benchmarks.
DeepSeek
MetaSummary
- They share 16 benchmarks with published results for both. DeepSeek V4.1 Flash scores higher in 8 categories and Llama 3-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek V4.1 Flash leads 48.4 to 8.3.
- The biggest single-benchmark swing is DTBench: 89.9% for DeepSeek V4.1 Flash and 43.9% for Llama 3-8B.
- Llama 3-8B is cheaper at $0.14 / $0.14 per million input/output tokens, against $0.15 / $0.60 for DeepSeek V4.1 Flash.
- DeepSeek V4.1 Flash accepts more context: 1M tokens versus 8K.
Side by side
| DeepSeek V4.1 Flash | Llama 3-8B | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 50.3 | 25.3 |
| Released | 2026-09-09 | 2024-04-18 |
| Weights | Open | Open |
| Context window | 1M | 8K |
| Max output | 393K | 8K |
| Input $ / M tokens | $0.15 | $0.14 |
| Output $ / M tokens | $0.60 | $0.14 |
| Results tracked | 25 | 34 |
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Category by category
Coding DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 52.8 (#29), Llama 3-8B: 30.5 (#285)
| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1511 | 1152 |
| LMArena WebDev | 1620 | — |
| SciCode | 51.9% | — |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| ALE-Bench | 1,092 | — |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |
Reasoning DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 46.2 (#39), Llama 3-8B: 13.8 (#321)
| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| LMArena Hard Prompts | 1485 | 1133 |
| DTBench | 89.9% | 43.9% |
| Epoch Capabilities Index | 155.01 | 116.33 |
| NYT Connections (extended) | 89.6% | — |
| CritPt | 14.3% | — |
| Chess Puzzles | — | 0% |
| LMCA | 47% | — |
| Surface Evolver Bench | 46.3% | — |
| Adversarial NLI | — | 57.3% |
| ForecastBench | — | 58.6 |
| WinoGrande | — | 75.7% |
Math DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 48.4 (#52), Llama 3-8B: 8.3 (#317)
| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| LMArena Math | 1493 | 1151 |
| OTIS Mock AIME 2024-2025 | — | 1.9% |
| ProofBench | 54% | — |
| MATH Level 5 | — | 6.1% |
Knowledge DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 41.6 (#97), Llama 3-8B: 7.8 (#303)
| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| LMArena Expert | 1505 | 1113 |
| GPQA Diamond | — | 26.1% |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |
Multilingual DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 55.5 (#29), Llama 3-8B: 31.0 (#256)
| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1452 | 1098 |
| LMArena Chinese | 1506 | 1076 |
| LMArena French | 1448 | 1158 |
| LMArena Russian | 1473 | 1109 |
| LMArena Spanish | 1499 | 1172 |
| LMArena German | — | 1104 |
| LMArena Japanese | — | 967 |
| LMArena Korean | — | 1003 |
Instruction Following DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 77.6 (#24), Llama 3-8B: 58.7 (#255)
| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1475 | 1127 |
Long Context DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 45.2 (#46), Llama 3-8B: 34.2 (#246)
| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1476 | 1128 |
Writing & Preference DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 65.8 (#43), Llama 3-8B: 37.7 (#251)
| Benchmark | DeepSeek V4.1 Flash | Llama 3-8B |
|---|---|---|
| LMArena Text | 1465 | 1166 |
| LMArena Creative Writing | 1436 | 1150 |
| LMArena Multi-Turn | 1460 | 1152 |
| EQ-Bench Creative Writing | 1540 | — |
Frequently asked questions
Is DeepSeek V4.1 Flash better than Llama 3-8B?
DeepSeek V4.1 Flash is the stronger model overall, scoring 50.3 to 25.3 on the Noometry Index. Llama 3-8B costs 1.9× less per token, which makes it the better buy when DeepSeek V4.1 Flash's lead doesn't matter for your workload.
Which is cheaper, DeepSeek V4.1 Flash or Llama 3-8B?
Llama 3-8B is cheaper. It lists at $0.14 per million input tokens and $0.14 per million output tokens; DeepSeek V4.1 Flash lists at $0.15 and $0.60.
Is DeepSeek V4.1 Flash or Llama 3-8B better for coding?
DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.8 versus 30.5 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4.1 Flash does, with 1M tokens against 8K.
How many benchmarks do DeepSeek V4.1 Flash and Llama 3-8B share?
16 benchmarks have published results for both models. DeepSeek V4.1 Flash has 25 scored results on Noometry and Llama 3-8B has 34.