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DeepSeek V3.2 vs Llama 3.3 70B Instruct

DeepSeek V3.2

DeepSeek

81#81
vs
Signal-by-Signal Comparison
SignalDeepSeek V3.2DeltaLlama 3.3 70B Instruct
Capabilities
67
+17
50
Benchmarks
83
+12
71
Pricing
100
0
100
Context window size
83
+2
81
Recency
87
+66
22
Output Capacity
87
+17
70
Overall Result
5 wins
of 6
1 wins
DeepSeek V3.2 wins 5 of 6 signals

Score History

Score History (25 data points)
DeepSeek V3.2Llama 3.3 70B Instruct
DeepSeek V3.2

81.3

current score

Leader

DeepSeek V3.2

right now

Llama 3.3 70B Instruct

66.8

current score

LMMarketCap.com
Interactive Price Comparison
100Kcalls/month
1,000tokens (~1,333 chars)
500tokens (~667 chars)

DeepSeek V3.2

DeepSeek

Per request$0.000450
Daily$1.50
Monthly$45.00
Annual$540.00

Llama 3.3 70B Instruct

Meta

Best Value
Per request$0.000260
Daily$0.87
Monthly$26.00
Annual$312.00

Llama 3.3 70B Instruct saves you $19.00/month

That's $228.00/year compared to DeepSeek V3.2 at your current usage level of 100K calls/month.

42% cheaper
Choose Llama 3.3 70B Instruct for cost optimization

DeepSeek V3.2 pricing:
Input:$0.26/M tokens
Output:$0.38/M tokens
Llama 3.3 70B Instruct pricing:
Input:$0.10/M tokens
Output:$0.32/M tokens
Winner
DeepSeek V3.2

DeepSeek

81

Composite Score

Llama 3.3 70B Instruct

Meta

67

Composite Score

Signal-by-Signal Comparison
MetricDeepSeek V3.2Llama 3.3 70B InstructWinner
Overall Score
81
67
DeepSeek V3.2
Rank#81#180
DeepSeek V3.2
Quality Rank#81#180
DeepSeek V3.2
Adoption Rank#81#180
DeepSeek V3.2
Parameters--70B--
Context Window164K131K
DeepSeek V3.2
Pricing$0.26/$0.38/M$0.10/$0.32/M--
Signal Scores
Capabilities
67
50
DeepSeek V3.2
Benchmarks
83
71
DeepSeek V3.2
Pricing
100
100
Llama 3.3 70B Instruct
Context window size
83
81
DeepSeek V3.2
Recency
87
22
DeepSeek V3.2
Output Capacity
87
70
DeepSeek V3.2
Benchmark Head-to-Head(10 benchmarks)
DeepSeek V3.2: 4Llama 3.3: 0
DeepSeek V3.2
Llama 3.3
Normalized 0-100%
MMLU
88.5%86.3%
MMLU-Pro
85%68.9%
GPQA Diamond
85.7%50.5%
MATH-500
-77%
HumanEval
-88.4%
SWE-bench Verified
70%-
IFEval
-92.1%
BBH
-82.8%
Arena Elo
14251243
BigCodeBench
-46.9%
Benchmark Interpretation

Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.

DeepSeek V3.2Strong Performer

Scores 81/100 (rank #81), placing it in the top 72% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100
Llama 3.3 70B InstructCompetitive

Scores 67/100 (rank #180), placing it in the top 38% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100

DeepSeek V3.2 has a 15-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.

When to Use Each Model

Choose DeepSeek V3.2 when you need:

  • Step-by-step reasoning and chain-of-thought problem solving
  • Self-hosted deployments where you need full control over the model

Choose Llama 3.3 70B Instruct when you need:

  • Self-hosted deployments where you need full control over the model
Cost-Performance Analysis
DeepSeek V3.2
Input cost$0.26/M tokens
Output cost$0.38/M tokens
Cost per quality point$0.008
Est. monthly (1M tokens/day)$9.60
Llama 3.3 70B InstructBest Value
Input cost$0.10/M tokens
Output cost$0.32/M tokens
Cost per quality point$0.006
Est. monthly (1M tokens/day)$6.30

Llama 3.3 70B Instruct offers 34% better value per quality point. At 1M tokens/day, you'd spend $6.30/month with Llama 3.3 70B Instruct vs $9.60/month with DeepSeek V3.2 - a $3.30 monthly difference.

Latency & Speed
DeepSeek V3.2Faster
Speed score0/100
Llama 3.3 70B Instruct
Speed score0/100

Both models have comparable response speeds. For most applications, the latency difference is negligible.

When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.

Example Use Cases

Code generation & review

Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring

DeepSeek V3.2

Customer support chatbot

Suitable for user-facing chat with competitive response times. Llama 3.3 70B Instruct also offers lower per-token costs for high-volume support

DeepSeek V3.2

Long document analysis

Larger context window (164K tokens) can process longer documents, contracts, and research papers in a single pass

DeepSeek V3.2

Batch data extraction

Lower output pricing ($0.32/M) reduces costs when processing thousands of records daily

Llama 3.3 70B Instruct

Creative writing & content

Higher overall composite score (81/100) correlates with better nuance, coherence, and style in long-form content

DeepSeek V3.2
Which Should You Choose?
Our recommendation:
DeepSeek V3.2

DeepSeek V3.2 clearly outperforms Llama 3.3 70B Instruct with a significant 14.5-point lead. For most general use cases, DeepSeek V3.2 is the stronger choice. However, Llama 3.3 70B Instruct may still excel in niche scenarios.

DeepSeek V3.2
Recommended

by DeepSeek

  • Choose for Quality - Marginally better benchmark scores; both are excellent
  • Choose for Reliability - Higher uptime and faster response speeds
  • Choose for Prototyping - Stronger community support and better developer experience
  • Choose for Production - Wider enterprise adoption and proven at scale

by Meta

  • Choose for Cost - 34% lower pricing; better value at scale
Capability Comparison
CapabilityDeepSeek V3.2Llama 3.3 70B Instruct
Vision (Image Input)
Function Calling
Streaming
JSON Mode
Reasoningdiffers
Web Search
Image Output
Monthly Cost Calculator
1,000tokens (600 in / 400 out)
100requests/day (3,000/month)

DeepSeek V3.2

DeepSeek

$0.9240
estimated monthly cost

Llama 3.3 70B Instruct

Meta

Best Value
$0.5640
estimated monthly cost

Llama 3.3 70B Instruct saves you $0.3600/month

That's 39% cheaper than DeepSeek V3.2 at 1,000 tokens/request and 100 requests/day.

Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.

Parameters & Context
ParameterDeepSeek V3.2Llama 3.3 70B Instruct
Context Window164K131K
Max Output Tokens163,84016,384
Open SourceYesYes
CreatedDec 1, 2025Dec 6, 2024
Last updated: 40m ago

相关对比

DeepSeek V3.2 vs Llama 3.3 70B Instruct (2026) | LM Market Cap