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DeepSeek-V4.1-Flash vs Llama 3.1 70B Instruct

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 7.6. Llama 3.1 70B Instruct is 1.6x cheaper per token.

DeepSeek · Meta · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 7.6, ranking #12 overall.

In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 1; this is a narrower head-to-head signal than the composite indexes.

On price, Llama 3.1 70B Instruct is roughly 1.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

DeepSeek-V4.1-Flash also accepts a larger context window (1,040,000 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose DeepSeek-V4.1-Flash

  • overall performance matters — it scores 51.8 and ranks #12 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you process long inputs — it offers a 1,040,000 token context window
  • you want the most recent training data — it shipped Sep 2026

Choose Llama 3.1 70B Instruct

  • cost matters — it's about 1.6x cheaper per token

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash
DeepSeek
Llama 3.1 70B Instruct
Meta
Core performance indexes
LLM Stats Score
51.8
#12
7.6
#285
Reasoning
48.9
#17
6.3
#287
Coding
44.4
#5
3.6
#214
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
$0.22 / M
$0.20 / M
Output price
$0.66 / M
$0.20 / M
Context window
1,040,000
128,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
DeepSeek-V4.1-Flash
Llama 3.1 70B Instruct
Math
35.2#43
11.4#240
Tool use
35.1#2
24.5#35
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V4.1-Flash · 18 for Llama 3.1 70B Instruct

1 shared

DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while Llama 3.1 70B Instruct is better at 0 benchmarks.

DeepSeek-V4.1-Flash significantly outperforms across most benchmarks.

Fri Sep 11 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Llama 3.1 70B Instruct costs less

For input processing, DeepSeek-V4.1-Flash ($0.22/1M tokens) is 1.1x more expensive than Llama 3.1 70B Instruct ($0.20/1M tokens).

For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 3.3x more expensive than Llama 3.1 70B Instruct ($0.20/1M tokens).

In conclusion, DeepSeek-V4.1-Flash is more expensive than Llama 3.1 70B Instruct.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Fri Sep 11 2026 • llm-stats.com
DeepSeek
DeepSeek-V4.1-Flash
Input tokens$0.22
Output tokens$0.66
Best providerFireworks
Meta
Llama 3.1 70B Instruct
Input tokens$0.20
Output tokens$0.20
Best providerLambda
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

693.2B diff

DeepSeek-V4.1-Flash has 693.2B more parameters than Llama 3.1 70B Instruct, making it 990.3% larger.

DeepSeek
DeepSeek-V4.1-Flash
763.2Bparameters
Meta
Llama 3.1 70B Instruct
70.0Bparameters
763.2B
DeepSeek-V4.1-Flash
70.0B
Llama 3.1 70B Instruct

Context Window

Maximum input and output token capacity

DeepSeek-V4.1-Flash accepts 1,040,000 input tokens compared to Llama 3.1 70B Instruct's 128,000 tokens. DeepSeek-V4.1-Flash can generate longer responses up to 393,216 tokens, while Llama 3.1 70B Instruct is limited to 128,000 tokens.

DeepSeek
DeepSeek-V4.1-Flash
Input1,040,000 tokens
Output393,216 tokens
Meta
Llama 3.1 70B Instruct
Input128,000 tokens
Output128,000 tokens
Fri Sep 11 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash supports multimodal inputs, whereas Llama 3.1 70B Instruct does not.

DeepSeek-V4.1-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4.1-Flash

Text
Images
Audio
Video

Llama 3.1 70B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash is licensed under MIT, while Llama 3.1 70B Instruct uses Llama 3.1 Community License.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V4.1-Flash

MIT

Open weights

Llama 3.1 70B Instruct

Llama 3.1 Community License

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash was released on 2026-09-10, while Llama 3.1 70B Instruct was released on 2024-07-23.

DeepSeek-V4.1-Flash is 26 months newer than Llama 3.1 70B Instruct.

DeepSeek-V4.1-Flash

Sep 10, 2026

1 days ago

2.1yr newer
Llama 3.1 70B Instruct

Jul 23, 2024

2.1 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

DeepSeek-V4.1-Flash is available from Fireworks, DeepInfra, DeepSeek, Novita. Llama 3.1 70B Instruct is available from Lambda, DeepInfra, Hyperbolic, Groq, Cerebras, Together, Fireworks, Bedrock, Sambanova.

DeepSeek-V4.1-Flash

fireworks logo
Fireworks
Input Price:Input: $0.22/1MOutput Price:Output: $0.66/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M
deepseek logo
DeepSeek
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M
novita logo
Novita
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M

Llama 3.1 70B Instruct

lambda logo
Lambda
Input Price:Input: $0.20/1MOutput Price:Output: $0.20/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.35/1MOutput Price:Output: $0.40/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $0.40/1MOutput Price:Output: $0.40/1M
groq logo
Groq
Input Price:Input: $0.59/1MOutput Price:Output: $0.78/1M
cerebras logo
Cerebras
Input Price:Input: $0.60/1MOutput Price:Output: $0.60/1M
together logo
Together
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
fireworks logo
Fireworks
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
bedrock logo
AWS Bedrock
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
sambanova logo
Sambanova
Input Price:Input: $5.00/1MOutput Price:Output: $10.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash and Llama 3.1 70B Instruct side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash
✓ Preferred
Llama 3.1 70B Instruct
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash vs Llama 3.1 70B Instruct.

Which is better, DeepSeek-V4.1-Flash or Llama 3.1 70B Instruct?

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 7.6. DeepSeek-V4.1-Flash is made by DeepSeek and Llama 3.1 70B Instruct is made by Meta. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek-V4.1-Flash compare to Llama 3.1 70B Instruct in benchmarks?

DeepSeek-V4.1-Flash scores CodeForces: 100.0%, GPQA: 90.9%, Terminal-Bench 2.1: 90.6%, BabyVision: 89.6%, CyberGym: 88.1%. Llama 3.1 70B Instruct scores GSM-8K (CoT): 95.1%, ARC-C: 94.8%, API-Bank: 90.0%, IFEval: 87.5%, Multilingual MGSM (CoT): 86.9%.

Is DeepSeek-V4.1-Flash cheaper than Llama 3.1 70B Instruct?

Llama 3.1 70B Instruct is 1.1x cheaper for input tokens. DeepSeek-V4.1-Flash costs $0.22/M input and $0.66/M output via fireworks. Llama 3.1 70B Instruct costs $0.20/M input and $0.20/M output via lambda.

What are the context window sizes for DeepSeek-V4.1-Flash and Llama 3.1 70B Instruct?

DeepSeek-V4.1-Flash supports 1.0M tokens and Llama 3.1 70B Instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V4.1-Flash and Llama 3.1 70B Instruct?

Key differences include LLM Stats Score (51.8 vs 7.6), context window (1.0M vs 128K), input pricing ($0.22 vs $0.20/M), multimodal support (yes vs no), licensing (MIT vs Llama 3.1 Community License). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash and Llama 3.1 70B Instruct?

DeepSeek-V4.1-Flash is developed by DeepSeek and Llama 3.1 70B Instruct is developed by Meta.

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