| Signal | Olmo 3 32B Think | Delta | Llama 4 Scout |
|---|---|---|---|
Capabilities | 50 | -17 | |
Benchmarks | 54 | +3 | |
Pricing | 100 | 0 | |
Context window size | 76 | -11 | |
Recency | 100 | +33 | |
Output Capacity | 80 | +10 | |
| Overall Result | 3 wins | of 6 | 3 wins |
Score History
55
current score
Olmo 3 32B Think
right now
54.2
current score
Allen AI
Meta
Llama 4 Scout saves you $17.00/month
That's $204.00/year compared to Olmo 3 32B Think at your current usage level of 100K calls/month.
| Metric | Olmo 3 32B Think | Llama 4 Scout | Winner |
|---|---|---|---|
| Overall Score | 55 | 54 | Olmo 3 32B Think |
| Rank | #106 | #107 | Olmo 3 32B Think |
| Quality Rank | #106 | #107 | Olmo 3 32B Think |
| Adoption Rank | #106 | #107 | Olmo 3 32B Think |
| Parameters | 32B | -- | -- |
| Context Window | 66K | 328K | Llama 4 Scout |
| Pricing | $0.15/$0.50/M | $0.08/$0.30/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 67 | Llama 4 Scout |
| Benchmarks | 54 | 52 | Olmo 3 32B Think |
| Pricing | 100 | 100 | Llama 4 Scout |
| Context window size | 76 | 88 | Llama 4 Scout |
| Recency | 100 | 67 | Olmo 3 32B Think |
| Output Capacity | 80 | 70 | Olmo 3 32B Think |
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 55/100 (rank #106), placing it in the top 64% of all 290 models tracked.
Scores 54/100 (rank #107), placing it in the top 63% 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.
Llama 4 Scout offers 42% better value per quality point. At 1M tokens/day, you'd spend $5.70/month with Llama 4 Scout vs $9.75/month with Olmo 3 32B Think - a $4.05 monthly difference.
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 4 Scout also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (328K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.30/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (55/100) 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
Olmo 3 32B Think and Llama 4 Scout 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
Olmo 3 32B Think
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 4 Scout
42% lower pricing; better value at scale
Best for Reliability
Olmo 3 32B Think
Higher uptime and faster response speeds
Best for Prototyping
Olmo 3 32B Think
Stronger community support and better developer experience
Best for Production
Olmo 3 32B Think
Wider enterprise adoption and proven at scale
by Allen AI
| Capability | Olmo 3 32B Think | Llama 4 Scout |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Allen AI
Meta
Llama 4 Scout saves you $0.3660/month
That's 42% cheaper than Olmo 3 32B Think 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 | Olmo 3 32B Think | Llama 4 Scout |
|---|---|---|
| Context Window | 66K | 328K |
| Max Output Tokens | 65,536 | 16,384 |
| Open Source | Yes | Yes |
| Created | Nov 21, 2025 | Apr 5, 2025 |
Olmo 3 32B Think scores 55/100 (rank #106) compared to Llama 4 Scout's 54/100 (rank #107), giving it a 1-point advantage. Olmo 3 32B Think is the stronger overall choice, though Llama 4 Scout may excel in specific areas like cost efficiency.
Olmo 3 32B Think is ranked #106 and Llama 4 Scout is ranked #107 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 4 Scout is cheaper at $0.30/M output tokens vs Olmo 3 32B Think's $0.50/M output tokens - 1.7x more expensive. Input token pricing: Olmo 3 32B Think at $0.15/M vs Llama 4 Scout at $0.08/M.
Llama 4 Scout has a larger context window of 327,680 tokens compared to Olmo 3 32B Think's 65,536 tokens. A larger context window means the model can process longer documents and conversations.