Model comparison
DeepSeek V4 Flash vs Llama 3.1-8B
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 22.8 on the Noometry Index. Llama 3.1-8B costs 4.6× less per token, which makes it the better buy when DeepSeek V4 Flash's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
DeepSeek
MetaSummary
- They share 27 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 8 categories and Llama 3.1-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek V4 Flash leads 60.0 to 9.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 94.4% for DeepSeek V4 Flash and 1.7% for Llama 3.1-8B.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.15 / $0.60 for DeepSeek V4 Flash.
- DeepSeek V4 Flash accepts more context: 1M tokens versus 128K.
Side by side
| DeepSeek V4 Flash | Llama 3.1-8B | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 53.6 | 22.8 |
| Released | 2026-04-24 | 2024-07-23 |
| Weights | Open | Open |
| Context window | 1M | 128K |
| Max output | 393K | 4K |
| Input $ / M tokens | $0.15 | $0.05 |
| Output $ / M tokens | $0.60 | $0.08 |
| Results tracked | 41 | 43 |
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Category by category
Coding DeepSeek V4 Flash leads
DeepSeek V4 Flash: 47.6 (#56), Llama 3.1-8B: 19.9 (#335)
| Benchmark | DeepSeek V4 Flash | Llama 3.1-8B |
|---|---|---|
| SciCode | 49.9% | 13.2% |
| WeirdML | 63% | 1.7% |
| LMArena Coding | 1457 | 1195 |
| FrontierCode | 18.8% | — |
| LMArena WebDev | 1581 | — |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 1,306 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |
Agentic & Tool Use Not comparable
DeepSeek V4 Flash: —, Llama 3.1-8B: 22.3 (#123)
| Benchmark | DeepSeek V4 Flash | Llama 3.1-8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 25.8% |
| BALROG | — | 15.1% |
Reasoning DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.7 (#25), Llama 3.1-8B: 14.5 (#316)
| Benchmark | DeepSeek V4 Flash | Llama 3.1-8B |
|---|---|---|
| CritPt | 16.6% | 0% |
| Chess Puzzles | 33% | 0% |
| LMArena Hard Prompts | 1443 | 1175 |
| DTBench | 90.9% | 50.9% |
| LMCA | 41.7% | 5.4% |
| Epoch Capabilities Index | 154.51 | 116.48 |
| ARC-AGI-2 | 61.4% | — |
| SimpleBench | 61.1% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 89.6% | — |
| ARC-AGI-1 | 89% | — |
| Mystery Game Puzzles | 34% | — |
| PIQA | — | 81.2% |
Math DeepSeek V4 Flash leads
DeepSeek V4 Flash: 60.0 (#31), Llama 3.1-8B: 9.5 (#311)
| Benchmark | DeepSeek V4 Flash | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 94.4% | 1.7% |
| LMArena Math | 1427 | 1179 |
| FrontierMath (Tiers 1-3) | 57.5% | — |
| FrontierMath Tier 4 | 24.4% | — |
| MathArena Final-Answer Competitions | 76.5% | — |
| ProofBench | 56% | — |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| GSM8K | — | 82.4% |
Knowledge DeepSeek V4 Flash leads
DeepSeek V4 Flash: 55.5 (#43), Llama 3.1-8B: 8.0 (#302)
| Benchmark | DeepSeek V4 Flash | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 91% | 27% |
| LMArena Expert | 1441 | 1144 |
| SimpleQA Verified | 33.6% | — |
| MMLU-Pro | — | 40.6% |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |
Multilingual DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.2 (#71), Llama 3.1-8B: 34.2 (#244)
| Benchmark | DeepSeek V4 Flash | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1420 | 1148 |
| LMArena Chinese | 1467 | 1151 |
| LMArena French | 1438 | 1176 |
| LMArena German | 1418 | 1144 |
| LMArena Japanese | 1403 | 1061 |
| LMArena Korean | 1383 | 1053 |
| LMArena Russian | 1428 | 1158 |
| LMArena Spanish | 1437 | 1169 |
Instruction Following DeepSeek V4 Flash leads
DeepSeek V4 Flash: 75.1 (#76), Llama 3.1-8B: 59.1 (#253)
| Benchmark | DeepSeek V4 Flash | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1421 | 1159 |
| IFEval | — | 74.3% |
Long Context DeepSeek V4 Flash leads
DeepSeek V4 Flash: 43.8 (#81), Llama 3.1-8B: 35.8 (#233)
| Benchmark | DeepSeek V4 Flash | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1435 | 1182 |
Writing & Preference DeepSeek V4 Flash leads
DeepSeek V4 Flash: 64.0 (#59), Llama 3.1-8B: 29.9 (#285)
| Benchmark | DeepSeek V4 Flash | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1432 | 1187 |
| LMArena Creative Writing | 1403 | 1154 |
| EQ-Bench Creative Writing | 1559 | 713 |
| LMArena Multi-Turn | 1449 | 1172 |
| WildBench | — | 68.7% |
Frequently asked questions
Is DeepSeek V4 Flash better than Llama 3.1-8B?
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 22.8 on the Noometry Index. Llama 3.1-8B costs 4.6× less per token, which makes it the better buy when DeepSeek V4 Flash's lead doesn't matter for your workload.
Which is cheaper, DeepSeek V4 Flash or Llama 3.1-8B?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; DeepSeek V4 Flash lists at $0.15 and $0.60.
Is DeepSeek V4 Flash or Llama 3.1-8B better for coding?
DeepSeek V4 Flash scores higher on coding benchmarks: 47.6 versus 19.9 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4 Flash does, with 1M tokens against 128K.
How many benchmarks do DeepSeek V4 Flash and Llama 3.1-8B share?
27 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and Llama 3.1-8B has 43.