| Signal | Llama 3.3 70B Instruct (free) | Delta | Phi 4 |
|---|---|---|---|
Capabilities | 33 | -- | |
Benchmarks | 71 | -2 | |
Pricing | 100 | +0 | |
Context window size | 76 | +10 | |
Recency | 44 | -6 | |
Output Capacity | 20 | -50 | |
| Overall Result | 2 wins | of 6 | 3 wins |
Score History
65.7
current score
Phi 4
right now
66.5
current score
Meta
Microsoft
Llama 3.3 70B Instruct (free) saves you $13.50/month
That's $162.00/year compared to Phi 4 at your current usage level of 100K calls/month.
| Metric | Llama 3.3 70B Instruct (free) | Phi 4 | Winner |
|---|---|---|---|
| Overall Score | 66 | 67 | Phi 4 |
| Rank | #105 | #103 | Phi 4 |
| Quality Rank | #105 | #103 | Phi 4 |
| Adoption Rank | #105 | #103 | Phi 4 |
| Parameters | 70B | -- | -- |
| Context Window | 66K | 16K | Llama 3.3 70B Instruct (free) |
| Pricing | Free | $0.07/$0.14/M | -- |
| Signal Scores | |||
| Capabilities | 33 | 33 | Llama 3.3 70B Instruct (free) |
| Benchmarks | 71 | 73 | Phi 4 |
| Pricing | 100 | 100 | Llama 3.3 70B Instruct (free) |
| Context window size | 76 | 67 | Llama 3.3 70B Instruct (free) |
| Recency | 44 | 51 | Phi 4 |
| Output Capacity | 20 | 70 | Phi 4 |
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 66/100 (rank #105), placing it in the top 64% of all 290 models tracked.
Scores 67/100 (rank #103), placing it in the top 65% of all 290 models tracked.
With only a 1-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
Compare the cost per quality point to find the best value for your specific workload.
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
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. Llama 3.3 70B Instruct (free) also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (66K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.00/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 (free) and Phi 4 are extremely close in overall performance (only 0.7999999999999972 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Llama 3.3 70B Instruct (free)
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3.3 70B Instruct (free)
100% lower pricing; better value at scale
Best for Reliability
Llama 3.3 70B Instruct (free)
Higher uptime and faster response speeds
Best for Prototyping
Llama 3.3 70B Instruct (free)
Stronger community support and better developer experience
Best for Production
Llama 3.3 70B Instruct (free)
Wider enterprise adoption and proven at scale
by Meta
| Capability | Llama 3.3 70B Instruct (free) | Phi 4 |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Meta
Microsoft
Llama 3.3 70B Instruct (free) saves you $0.2850/month
That's 100% cheaper than Phi 4 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.
| Parameter | Llama 3.3 70B Instruct (free) | Phi 4 |
|---|---|---|
| Context Window | 66K | 16K |
| Max Output Tokens | -- | 16,384 |
| Open Source | Yes | Yes |
| Created | Dec 6, 2024 | Jan 10, 2025 |
Phi 4 scores 67/100 (rank #103) compared to Llama 3.3 70B Instruct (free)'s 66/100 (rank #105), giving it a 1-point advantage. Phi 4 is the stronger overall choice, though Llama 3.3 70B Instruct (free) may excel in specific areas like cost efficiency.
Llama 3.3 70B Instruct (free) is ranked #105 and Phi 4 is ranked #103 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
Llama 3.3 70B Instruct (free) is cheaper at $0.00/M output tokens vs Phi 4's $0.14/M output tokens - 140.0x more expensive. Input token pricing: Llama 3.3 70B Instruct (free) at $0.00/M vs Phi 4 at $0.07/M.
Llama 3.3 70B Instruct (free) has a larger context window of 65,536 tokens compared to Phi 4's 16,384 tokens. A larger context window means the model can process longer documents and conversations.