| Signal | Llama 3.3 70B Instruct | Delta | Phi 4 Mini Instruct |
|---|---|---|---|
Capabilities | 50 | +17 | |
Benchmarks | 71 | +6 | |
Pricing | 100 | -- | |
Context window size | 81 | +0 | |
Recency | 38 | -57 | |
Output Capacity | 70 | -15 | |
| Overall Result | 3 wins | of 6 | 2 wins |
Score History
66.8
current score
Llama 3.3 70B Instruct
right now
52.7
current score
Meta
Microsoft
Phi 4 Mini Instruct saves you $0.50/month
That's $6.00/year compared to Llama 3.3 70B Instruct at your current usage level of 100K calls/month.
| Metric | Llama 3.3 70B Instruct | Phi 4 Mini Instruct | Winner |
|---|---|---|---|
| Overall Score | 67 | 53 | Llama 3.3 70B Instruct |
| Rank | #120 | #167 | Llama 3.3 70B Instruct |
| Quality Rank | #120 | #167 | Llama 3.3 70B Instruct |
| Adoption Rank | #120 | #167 | Llama 3.3 70B Instruct |
| Parameters | 70B | -- | -- |
| Context Window | 131K | 128K | Llama 3.3 70B Instruct |
| Pricing | $0.10/$0.32/M | $0.08/$0.35/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 33 | Llama 3.3 70B Instruct |
| Benchmarks | 71 | 65 | Llama 3.3 70B Instruct |
| Pricing | 100 | 100 | Llama 3.3 70B Instruct |
| Context window size | 81 | 81 | Llama 3.3 70B Instruct |
| Recency | 38 | 96 | Phi 4 Mini Instruct |
| Output Capacity | 70 | 85 | Phi 4 Mini Instruct |
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.
Scores 67/100 (rank #120), placing it in the top 59% of all 290 models tracked.
Scores 53/100 (rank #167), placing it in the top 43% of all 290 models tracked.
Llama 3.3 70B Instruct has a 14-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
Both models are priced similarly, so the decision comes down to quality and features rather than cost.
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.
Code generation & review
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
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
Long document analysis
Larger context window (131K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.32/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (67/100) correlates with better nuance, coherence, and style in long-form content
Llama 3.3 70B Instruct clearly outperforms Phi 4 Mini Instruct with a significant 14.099999999999994-point lead. For most general use cases, Llama 3.3 70B Instruct is the stronger choice. However, Phi 4 Mini Instruct may still excel in niche scenarios.
Best for Quality
Llama 3.3 70B Instruct
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3.3 70B Instruct
2% lower pricing; better value at scale
Best for Reliability
Llama 3.3 70B Instruct
Higher uptime and faster response speeds
Best for Prototyping
Llama 3.3 70B Instruct
Stronger community support and better developer experience
Best for Production
Llama 3.3 70B Instruct
Wider enterprise adoption and proven at scale
by Meta
| Capability | Llama 3.3 70B Instruct | Phi 4 Mini Instruct |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Meta
Microsoft
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Llama 3.3 70B Instruct | Phi 4 Mini Instruct |
|---|---|---|
| Context Window | 131K | 128K |
| Max Output Tokens | 16,384 | 128,000 |
| Open Source | Yes | Yes |
| Created | Dec 6, 2024 | Oct 17, 2025 |