Model comparison

DeepSeek V4.1 Flash vs Llama-3.3-70B-Instruct

DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 1.7× less per token, which makes it the better buy when DeepSeek V4.1 Flash's lead doesn't matter for your workload.

Last verified October 12, 2026. 24 shared benchmarks.

DeepSeek V4.1 Flash

DeepSeek

52.8

Rank #38 Confirmed

Llama-3.3-70B-Instruct

Summary

They share 24 benchmarks with published results for both. DeepSeek V4.1 Flash scores higher in 9 categories and Llama-3.3-70B-Instruct in 0 categories; 9 gaps are clear of the uncertainty.
The widest gap is in math, where DeepSeek V4.1 Flash leads 66.7 to 15.3.
The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.3% for DeepSeek V4.1 Flash and 5.1% for Llama-3.3-70B-Instruct.
Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 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 128K.

Side by side

DeepSeek V4.1 Flash and Llama-3.3-70B-Instruct specifications
	DeepSeek V4.1 Flash	Llama-3.3-70B-Instruct
Provider	DeepSeek	Meta
Noometry Index	52.8	30.6
Released	2026-09-09	2024-12-06
Weights	Open	Open
Context window	1M	128K
Max output	393K	4K
Input $ / M tokens	$0.15	$0.10
Output $ / M tokens	$0.60	$0.32
Results tracked	37	43

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Category by category

Coding DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 52.9 (#32), Llama-3.3-70B-Instruct: 31.0 (#290)

Coding benchmarks
Benchmark	DeepSeek V4.1 Flash	Llama-3.3-70B-Instruct
SciCode	51.9%	26%
LMArena Coding	1506	1268
LMArena WebDev	1619	—
WeirdML	—	14.4%
BigCodeBench Instruct	—	46.9%
LiveBench Coding	—	36.6%
BigCodeBench Complete	—	57.5%
ALE-Bench	1,092	—

Agentic & Tool Use DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 31.2 (#69), Llama-3.3-70B-Instruct: 25.8 (#105)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek V4.1 Flash	Llama-3.3-70B-Instruct
APEX-Agents	39.5%	—
Berkeley Function Calling Leaderboard	—	31.9%
BALROG	—	23%
GDP.pdf	19.8%	—

Reasoning DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 50.2 (#36), Llama-3.3-70B-Instruct: 14.1 (#327)

Reasoning benchmarks
Benchmark	DeepSeek V4.1 Flash	Llama-3.3-70B-Instruct
CritPt	14.3%	0%
LMArena Hard Prompts	1483	1257
DTBench	89.9%	59.5%
LMCA	47%	17.5%
Epoch Capabilities Index	154.9	127.33
SimpleBench	—	19.9%
NYT Connections (extended)	89.6%	—
LiveBench Reasoning	—	50.8%
Mystery Game Puzzles	43%	—
LiveBench Data Analysis	—	49.5%
Surface Evolver Bench	46.3%	—
ForecastBench	—	58.6
LiveBench	—	50.2%

Math DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 66.7 (#25), Llama-3.3-70B-Instruct: 15.3 (#298)

Math benchmarks
Benchmark	DeepSeek V4.1 Flash	Llama-3.3-70B-Instruct
OTIS Mock AIME 2024-2025	98.3%	5.1%
LMArena Math	1477	1267
FrontierMath (Tiers 1-3)	67.4%	—
FrontierMath Tier 4	26.8%	—
ProofBench	54%	—
LiveBench Math	—	42.2%
MATH Level 5	—	41.6%

Knowledge DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 57.9 (#38), Llama-3.3-70B-Instruct: 30.6 (#226)

Knowledge benchmarks
Benchmark	DeepSeek V4.1 Flash	Llama-3.3-70B-Instruct
GPQA Diamond	89.8%	47.4%
LMArena Expert	1506	1225
Confabulations	—	22.8%
Vectara Hallucination Rate	—	4.1%
MMLU	—	86.3%

Multimodal Not comparable

DeepSeek V4.1 Flash: 39.1 (#61), Llama-3.3-70B-Instruct: —

Multimodal benchmarks
Benchmark	DeepSeek V4.1 Flash	Llama-3.3-70B-Instruct
LMArena Vision	1277	—
Furniture Assembly	34.2%	—

Multilingual DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 55.0 (#35), Llama-3.3-70B-Instruct: 39.9 (#220)

Multilingual benchmarks
Benchmark	DeepSeek V4.1 Flash	Llama-3.3-70B-Instruct
LMArena Non-English	1448	1236
LMArena Chinese	1497	1217
LMArena French	1452	1281
LMArena German	1484	1251
LMArena Japanese	1412	1150
LMArena Korean	1452	1143
LMArena Russian	1471	1252
LMArena Spanish	1459	1270

Instruction Following DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 77.3 (#26), Llama-3.3-70B-Instruct: 71.1 (#157)

Instruction Following benchmarks
Benchmark	DeepSeek V4.1 Flash	Llama-3.3-70B-Instruct
LMArena Instruction Following	1474	1242
LiveBench Instruction Following	—	82.7%

Long Context DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 45.2 (#47), Llama-3.3-70B-Instruct: 26.4 (#295)

Long Context benchmarks
Benchmark	DeepSeek V4.1 Flash	Llama-3.3-70B-Instruct
LMArena Longer Query	1475	1256
Fiction.LiveBench	—	33.3%

Writing & Preference DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 65.4 (#48), Llama-3.3-70B-Instruct: 47.6 (#207)

Writing & Preference benchmarks
Benchmark	DeepSeek V4.1 Flash	Llama-3.3-70B-Instruct
LMArena Text	1462	1274
LMArena Creative Writing	1435	1250
LMArena Multi-Turn	1457	1280
EQ-Bench Creative Writing	1540	—
LiveBench Language	—	39.2%

Frequently asked questions

Is DeepSeek V4.1 Flash better than Llama-3.3-70B-Instruct?

Which is cheaper, DeepSeek V4.1 Flash or Llama-3.3-70B-Instruct?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; DeepSeek V4.1 Flash lists at $0.15 and $0.60.

Is DeepSeek V4.1 Flash or Llama-3.3-70B-Instruct better for coding?

DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.9 versus 31.0 in the Noometry coding category.

Which has the bigger context window?

DeepSeek V4.1 Flash does, with 1M tokens against 128K.

How many benchmarks do DeepSeek V4.1 Flash and Llama-3.3-70B-Instruct share?

24 benchmarks have published results for both models. DeepSeek V4.1 Flash has 37 scored results on Noometry and Llama-3.3-70B-Instruct has 43.