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Every score has a dated snapshot.

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  3. DeepSeek R1 vs Llama 3.3 70B

Pairwise benchmark snapshot · Aug 17, 2026

DeepSeek R1 vs Llama 3.3 70B benchmark

In this head-to-head showdown, DeepSeek R1 delivers higher overall intelligence and human-preferred responses (Elo 1,358 vs 1,285), while DeepSeek R1 leads in SWE-bench software engineering benchmarks (65.2%), while Llama 3.3 70B is more budget-friendly at $0.32/1M per 1M output tokens. Review the complete breakdown below to determine which model best fits your performance and budget requirements.

Executive Comparison Verdict

Bottom Line: Choose DeepSeek R1 if your priority is peak reasoning, complex code generation, and top preference Elo. Choose Llama 3.3 70B if you are optimizing for low latency, high throughput, and cost-efficient API deployment.
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DeepSeek
DeepSeek R1

Open-weights reasoning model trained with large-scale RL.

Elo 1,358$2.5/1M out
Meta
Llama 3.3 70B

Previous Meta 70B open-weight workhorse.

Elo 1,285$0.32/1M out

DeepSeek R1 Thinking Depth & Cost Scaler

Simulate accuracy gains vs added response time & token cost

Mode: Medium (Full RL CoT)

Reasoning models “think before answering” by generating internal reasoning tokens. Higher effort improves math, coding, and logic accuracy, but increases response delay and token costs.

Thinking Delay
420 ms
Standard speed
Reasoning Depth
~2,800
Internal tokens
Cost / 1k Calls
$8.10
7.4× standard bill
Accuracy Boost
+6.5% accuracy
STEM & SWE-bench
Top Rival Showdowns for DeepSeek R1
All Matchups
Compare vs:vs Claude Opus 4.5vs GPT-5vs Gemini 3 Provs Grok 4.6vs Qwen 3 Max
Preference Leader

DeepSeek R1

Δ 73 Arena Elo pts

Throughput Leader

Llama 3.3 70B

140 tok/s

Value per Dollar Leader

Llama 3.3 70B

$0.32 / 1M output

Capability Percentiles

Relative percentile scores computed across all active models in the benchmark catalog.

Capability Matchup Radar

Multi-Dimensional Capability Radar

Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.

Tap any node to inspect
Percentile 0–100
Dimensional AdvantageEven 3–3
Head-to-Head Comparison

Overall Matchup Breakdown

Category wins across reasoning intelligence, generation speed, and token cost.

DeepSeek R1 (4)vsLlama 3.3 70B (5)
DeepSeek R1: 4W (44%)Overall: Llama 3.3 70BLlama 3.3 70B: 5W (56%)
← DeepSeek R1Llama 3.3 70B →
DeepSeek R1 (4/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo
1,358vs1,285
Coding Elo
—vs—
SWE-bench
65.2%vs45.1%
LiveBench
59%vs49.8%
GPQA Diamond
79.8%vs65.7%
DeepSeek R1: 4WLlama 3.3 70B: 0W
Llama 3.3 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed
62 tok/svs140 tok/s
Time to first token
420 msvs160 ms
DeepSeek R1: 0WLlama 3.3 70B: 2W
Llama 3.3 70B (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price
$2.5/1Mvs$0.32/1M
Input price
$0.7/1Mvs$0.1/1M
Context window
64kvs131k
DeepSeek R1: 0WLlama 3.3 70B: 3W

Side-by-Side Benchmark Matrix

Preference EloDeepSeek R1 (+73)
DeepSeek R11,358
lmarena · Aug 17, 2026
Llama 3.3 70B1,285
lmarena · Aug 17, 2026
LiveBenchDeepSeek R1 (+9.2%)
DeepSeek R159%
livebench · Aug 17, 2026
Llama 3.3 70B49.8%
livebench · Aug 17, 2026
SWE-benchDeepSeek R1 (+20.1%)
DeepSeek R165.2%
swebench · Aug 17, 2026
Llama 3.3 70B45.1%
swebench · Aug 17, 2026
GPQA DiamondDeepSeek R1 (+14.1%)
DeepSeek R179.8%
seed-bootstrap · Aug 1, 2026
Llama 3.3 70B65.7%
seed-bootstrap · Aug 1, 2026
Time to first tokenLlama 3.3 70B (+260 ms)
DeepSeek R1420 ms
seed-bootstrap · Aug 1, 2026
Llama 3.3 70B160 ms
seed-bootstrap · Aug 1, 2026
Output speedLlama 3.3 70B (+78 tok/s)
DeepSeek R162 tok/s
seed-bootstrap · Aug 1, 2026
Llama 3.3 70B140 tok/s
seed-bootstrap · Aug 1, 2026
Input priceLlama 3.3 70B (+$0.6/1M)
DeepSeek R1$0.7/1M
openrouter · Aug 17, 2026
Llama 3.3 70B$0.1/1M
openrouter · Aug 17, 2026
Output priceLlama 3.3 70B (+$2.18/1M)
DeepSeek R1$2.5/1M
openrouter · Aug 17, 2026
Llama 3.3 70B$0.32/1M
openrouter · Aug 17, 2026
Context windowLlama 3.3 70B (+67k)
DeepSeek R164k
openrouter · Aug 17, 2026
Llama 3.3 70B131k
openrouter · Aug 17, 2026
BenchmarkDeepSeek R1Llama 3.3 70BAdvantage Delta
Preference Elo
1,358
lmarena · Aug 17, 2026
1,285
lmarena · Aug 17, 2026
DeepSeek R1 (+73)
LiveBench
59%
livebench · Aug 17, 2026
49.8%
livebench · Aug 17, 2026
DeepSeek R1 (+9.2%)
SWE-bench
65.2%
swebench · Aug 17, 2026
45.1%
swebench · Aug 17, 2026
DeepSeek R1 (+20.1%)
GPQA Diamond
79.8%
seed-bootstrap · Aug 1, 2026
65.7%
seed-bootstrap · Aug 1, 2026
DeepSeek R1 (+14.1%)
Time to first token
420 ms
seed-bootstrap · Aug 1, 2026
160 ms
seed-bootstrap · Aug 1, 2026
Llama 3.3 70B (+260 ms)
Output speed
62 tok/s
seed-bootstrap · Aug 1, 2026
140 tok/s
seed-bootstrap · Aug 1, 2026
Llama 3.3 70B (+78 tok/s)
Input price
$0.7/1M
openrouter · Aug 17, 2026
$0.1/1M
openrouter · Aug 17, 2026
Llama 3.3 70B (+$0.6/1M)
Output price
$2.5/1M
openrouter · Aug 17, 2026
$0.32/1M
openrouter · Aug 17, 2026
Llama 3.3 70B (+$2.18/1M)
Context window
64k
openrouter · Aug 17, 2026
131k
openrouter · Aug 17, 2026
Llama 3.3 70B (+67k)
Interactive Simulator (USD)

Workload Cost & Savings Calculator

Simulate monthly production API costs in USD (US Dollar).

Save up to 87% with Llama 3.3 70B
Monthly Volume50M tokens/mo
Quick Presets:
Token Ratio (In vs Out)70% In / 30% Out
RAG / Search (10% out)Coding / Chat (50% out)
DeepSeek R1$62.00 / mo
In: $24.5Out: $37.5
Llama 3.3 70B$8.30 / mo
In: $3.5Out: $4.8
Estimated Cost Delta

Llama 3.3 70B is estimated to save $53.70/month ($644/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

  • 1
    DeepSeek R1:Pick DeepSeek R1 when repo-level coding accuracy is the constraint.
  • 2
    Llama 3.3 70B:Pick Llama 3.3 70B when you are optimizing output cost.
  • 3
    Llama 3.3 70B:Pick Llama 3.3 70B when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsDeepSeek R1

Higher SWE-bench (65.2%).

High-volume chatLlama 3.3 70B

Lower output list price ($0.32/1M).

Voice / low-latency UILlama 3.3 70B

Lower TTFT (160 ms).

Long-document RAGLlama 3.3 70B

Larger window (131k).

Screenshots / visionDeepSeek R1

DeepSeek R1 is the side marked multimodal in the catalog.

Target WorkloadRecommended PickEvaluation Rationale
Repo / coding agentsDeepSeek R1Higher SWE-bench (65.2%).
High-volume chatLlama 3.3 70BLower output list price ($0.32/1M).
Voice / low-latency UILlama 3.3 70BLower TTFT (160 ms).
Long-document RAGLlama 3.3 70BLarger window (131k).
Screenshots / visionDeepSeek R1DeepSeek R1 is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news
launchJan 20, 2025

DeepSeek R1 stays as the open-weights RL baseline

Jan 2025 reasoning model. Still searched. V4 Pro is the newer DeepSeek buy.

launchDec 6, 2024

Llama 3.3 70B stays as the previous Meta 70B workhorse

Dec 2024 instruct 70B. Still a self-host baseline. Llama 4 is the 2026 buy.

Community Sentiment

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CompareLLM Matrix

Verified head-to-head card

LIVE
VS
DeepSeek

DeepSeek R1

Elo 1,358
LiveBench59%
SWE-bench65.2%
Speed62 tok/s
Output cost$2.5/1M
Meta

Llama 3.3 70B

Elo 1,285
LiveBench49.8%
SWE-bench45.1%
Speed140 tok/s
Output cost$0.32/1M

Frequently Asked Questions

Which is better overall, DeepSeek R1 or Llama 3.3 70B?
DeepSeek R1 has the higher preference Elo in our latest snapshot (1,358). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, DeepSeek R1 or Llama 3.3 70B?
DeepSeek R1 leads SWE-bench at 65.2% vs 45.1%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?
Llama 3.3 70B output tokens are $0.32/1M versus $2.5/1M. Input prices and retry rates still move the real bill.
Which is faster, DeepSeek R1 or Llama 3.3 70B?
Llama 3.3 70B has the lower time-to-first-token (160 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?
Llama 3.3 70B accepts 131k tokens versus 64k.
Can I self-host either model?
Both DeepSeek R1 and Llama 3.3 70B are marked open-weights. Hosting cost is not in this table.
Are these scores from CompareLLM’s own evals?
No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.
What does preference Elo mean on this page?
Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. DeepSeek R1 and Llama 3.3 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Related news

  • launch · Jan 20, 2025

    DeepSeek R1 stays as the open-weights RL baseline

  • launch · Dec 6, 2024

    Llama 3.3 70B stays as the previous Meta 70B workhorse

Similar Head-to-Head Comparisons

All DeepSeek R1 matchups →|All Llama 3.3 70B matchups →
DeepSeek R1 vs DeepSeek V3 (previous deepseek)DeepSeek R1 vs DeepSeek V4 Pro (next deepseek)Llama 3.3 70B vs Llama 3.1 70B (previous llama)Llama 3.3 70B vs Llama 4 Maverick (next llama)Llama 4 Maverick vs DeepSeek R1DeepSeek V4 Flash vs DeepSeek R1