| Signal | Gemma 4 31B | Delta | Llama 3.3 70B Instruct (free) |
|---|---|---|---|
Capabilities | 83 | +50 | |
Benchmarks | 86 | +15 | |
Pricing | 100 | 0 | |
Context window size | 86 | +10 | |
Recency | 100 | +57 | |
Output Capacity | 85 | +65 | |
| Overall Result | 5 wins | of 6 | 1 wins |
Score History
80.5
current score
Gemma 4 31B
right now
65.7
current score
Meta
Llama 3.3 70B Instruct (free) saves you $34.00/month
That's $408.00/year compared to Gemma 4 31B at your current usage level of 100K calls/month.
| Metric | Gemma 4 31B | Llama 3.3 70B Instruct (free) | Winner |
|---|---|---|---|
| Overall Score | 81 | 66 | Gemma 4 31B |
| Rank | #35 | #115 | Gemma 4 31B |
| Quality Rank | #35 | #115 | Gemma 4 31B |
| Adoption Rank | #35 | #115 | Gemma 4 31B |
| Parameters | 31B | 70B | -- |
| Context Window | 262K | 66K | Gemma 4 31B |
| Pricing | $0.14/$0.40/M | Free | -- |
| Signal Scores | |||
| Capabilities | 83 | 33 | Gemma 4 31B |
| Benchmarks | 86 | 71 | Gemma 4 31B |
| Pricing | 100 | 100 | Llama 3.3 70B Instruct (free) |
| Context window size | 86 | 76 | Gemma 4 31B |
| Recency | 100 | 43 | Gemma 4 31B |
| Output Capacity | 85 | 20 | Gemma 4 31B |
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 81/100 (rank #35), placing it in the top 88% of all 290 models tracked.
Scores 66/100 (rank #115), placing it in the top 61% of all 290 models tracked.
Gemma 4 31B has a 15-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 (free) also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (262K 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 (81/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Gemma 4 31B clearly outperforms Llama 3.3 70B Instruct (free) with a significant 14.799999999999997-point lead. For most general use cases, Gemma 4 31B is the stronger choice. However, Llama 3.3 70B Instruct (free) may still excel in niche scenarios.
Best for Quality
Gemma 4 31B
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
Gemma 4 31B
Higher uptime and faster response speeds
Best for Prototyping
Gemma 4 31B
Stronger community support and better developer experience
Best for Production
Gemma 4 31B
Wider enterprise adoption and proven at scale
by Google
| Capability | Gemma 4 31B | Llama 3.3 70B Instruct (free) |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Meta
Llama 3.3 70B Instruct (free) saves you $0.7320/month
That's 100% cheaper than Gemma 4 31B 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 | Gemma 4 31B | Llama 3.3 70B Instruct (free) |
|---|---|---|
| Context Window | 262K | 66K |
| Max Output Tokens | 131,072 | -- |
| Open Source | Yes | Yes |
| Created | Apr 2, 2026 | Dec 6, 2024 |