Model Comparison
V4 Pro (Reasoning, Max Effort)
vs. Llama 3.3 Instruct 70B
Comparing 2 AI models · 12 benchmarks · DeepSeek, Meta
Recommended Pick
De V4 Pro (Reasoning, Max Effort) 12 metric wins Strongest on: Value, Input price, Blended price
Best Value
V4 Pro (Reasoning, Max Effort)
100.0 value score
56.3 reasoning / $0.54/1M
Lowest Price
V4 Pro (Reasoning, Max Effort)
$0.43/1M input price
Best Reasoning
V4 Pro (Reasoning, Max Effort)
56.3 reasoning score
Blends available reasoning benchmarks
Best for Coding
V4 Pro (Reasoning, Max Effort)
59.4 coding index
Composite Indices
Higher is better; speed and price are normalized
Standard Benchmarks
Only benchmarks with data are shown
Differences That Matter
Best value
V4 Pro (Reasoning, Max Effort) has the strongest quality-to-price mix at 100.0 out of 100 value points.
Price gap
V4 Pro (Reasoning, Max Effort) is 1.3x cheaper on input tokens than Llama 3.3 Instruct 70B.
Speed gap
Llama 3.3 Instruct 70B generates about 1.1x as many tokens per second as V4 Pro (Reasoning, Max Effort).
Reasoning gap
V4 Pro (Reasoning, Max Effort) leads Llama 3.3 Instruct 70B by 29.9 points on reasoning.
Top-pick rationale
V4 Pro (Reasoning, Max Effort) wins 12 measurable categories, including Value, Input price, Blended price, Reasoning.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
V4 Pro (Reasoning, Max Effort)
DeepSeek
TTFT
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Time
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tok/s
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Tokens
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Cost
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Llama 3.3 Instruct 70B
Meta
TTFT
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Time
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tok/s
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Tokens
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Cost
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Which answer was more useful?
Full Comparison
| Metric | Top Pick De V4 Pro (Reasoning, Max Effort) | Me Llama 3.3 Instruct 70B |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.43/1M | $0.58/1M |
| Output Cost | $0.87/1M | $0.71/1M |
| Blended (3:1) | $0.54/1M | $0.61/1M |
| Specifications | ||
| Organization | DeepSeek | Meta |
| Release Date | Apr 24, 2026 | Dec 6, 2024 |
| Performance & Speed | ||
| Throughput | 89.2 tok/s | 95.3 tok/s |
| TTFT | 1000ms | 621ms |
| Latency | 50036ms | 621ms |
| Composite Indices | ||
| Value Score | 100.0 | 41.7 |
| Reasoning Score | 56.3 | 26.4 |
| Intelligence | 44.3 | 8.6 |
| Coding | 59.4 | — |
| Math | — | 7.7 |
| Standard Benchmarks | ||
| GPQA | 88.8% | 49.8% |
| MMLU Pro | — | 71.3% |
| HLE | 35.9% | 4.0% |
| LiveCodeBench | — | 28.8% |
| MATH 500 | — | 77.3% |
| AIME 2025 | — | 7.7% |
| AIME (Original) | — | 30.0% |
| SciCode | 50.0% | 26.0% |
| LCR | 66.3% | 15.0% |
| IFBench | 76.5% | 47.1% |
| TAU-bench v2 | 96.2% | 26.6% |
| TerminalBench Hard | 46.2% | 3.0% |
Key Takeaways
V4 Pro (Reasoning, Max Effort) offers the best value at $0.43/1M, making it ideal for high-volume applications and cost-conscious projects.
V4 Pro (Reasoning, Max Effort) has the strongest reasoning profile with a 56.3 reasoning score, combining the available reasoning-heavy benchmarks.
V4 Pro (Reasoning, Max Effort) reaches a 59.4 coding index, making it the top choice for software development and code generation tasks.
All models support context windows of ∞+ tokens, suitable for processing lengthy documents and maintaining extended conversations.
When to Choose Each Model
V4 Pro (Reasoning, Max Effort)
- Cost-sensitive applications
- High-volume processing
- Complex reasoning tasks
- Research & analysis
- Code generation
- Software development
Llama 3.3 Instruct 70B
- General-purpose AI
- Versatile applications
