Anthropic (14 models) vs Meta (Llama) (14 models) - compared across composite scores, pricing, capabilities, and context windows.
| Capability | Anthropic | Meta (Llama) | Leader |
|---|---|---|---|
Vision | 14/14 | 4/14 | Anthropic |
Reasoning | 12/14 | 0/14 | Anthropic |
Function Calling | 14/14 | 5/14 | Anthropic |
JSON Mode | 8/14 | 7/14 | Anthropic |
Web Search | 13/14 | 0/14 | Anthropic |
Streaming | 14/14 | 14/14 | Tie |
Image Output | 0/14 | 0/14 | Tie |
| Metric | Anthropic | Meta (Llama) |
|---|---|---|
| Cheapest Input (per 1M tokens) | $0.250 Claude 3 Haiku | $0.020 Llama Guard 3 8B |
| Cheapest Output (per 1M tokens) | $1.25 | $0.030 |
| Most Expensive Input (per 1M tokens) | $30.00 Claude Opus 4.6 (Fast) | $0.510 Llama 3 70B Instruct |
| Most Expensive Output (per 1M tokens) | $150.00 | $0.740 |
| Free Models | 0 | 2 |
| Max Context Window | 1.0M | 1.0M |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Claude Opus 4.6 (Fast) | 90 | $30.00 | $150.00 |
| Claude Opus 4.6 | 90 | $5.00 | $25.00 |
| Claude Sonnet 4.6 | 85 | $3.00 | $15.00 |
| Claude Opus 4.5 | 85 | $5.00 | $25.00 |
| Claude Sonnet 4.5 | 82 | $3.00 | $15.00 |
| Claude Opus 4 | 82 | $15.00 | $75.00 |
| Claude Opus 4.7 | 79 | $5.00 | $25.00 |
| Claude Opus 4.1 | 75 | $15.00 | $75.00 |
| Claude 3.7 Sonnet (thinking) | 75 | $3.00 | $15.00 |
| Claude Sonnet 4 | 74 | $3.00 | $15.00 |
| Claude 3.7 Sonnet | 73 | $3.00 | $15.00 |
| Claude Haiku 4.5 | 70 | $1.00 | $5.00 |
| Claude 3.5 Haiku | 58 | $0.800 | $4.00 |
| Claude 3 Haiku | 50 | $0.250 | $1.25 |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Llama 4 Maverick | 67 | $0.150 | $0.600 |
| Llama 3.3 70B Instruct | 67 | $0.100 | $0.320 |
| Llama 3.3 70B Instruct (free) | 66 | Free | Free |
| Llama 3.1 70B Instruct | 65 | $0.400 | $0.400 |
| Llama 3 70B Instruct | 57 | $0.510 | $0.740 |
| Llama 4 Scout | 54 | $0.080 | $0.300 |
| Llama 3.1 8B Instruct | 44 | $0.020 | $0.050 |
| Llama Guard 4 12B | 40 | $0.180 | $0.180 |
| Llama Guard 3 8B | 40 | $0.480 | $0.030 |
| Llama 3.2 11B Vision Instruct | 40 | $0.245 | $0.245 |
| Llama 3 8B Instruct | 34 | $0.040 | $0.040 |
| Llama 3.2 3B Instruct (free) | 33 | Free | Free |
| Llama 3.2 3B Instruct | 33 | $0.051 | $0.340 |
| Llama 3.2 1B Instruct | 18 | $0.027 | $0.200 |
Compare any two AI providers side-by-side.
Anthropic's models average 55/100 performance versus Llama's 34/100, with Claude Sonnet 4.6 hitting 66/100 compared to Llama 4 Maverick's 54/100. The premium buys you 100% vision and function calling coverage across all 13 Anthropic models, while Llama only offers vision on 4/14 models and function calling on 7/14.
Meta's 14 models have zero reasoning capability (0/14) and no web search integration (0/14), while Anthropic provides reasoning in 11/13 models and web search in 12/13. This makes Llama unsuitable for complex analytical tasks or real-time information retrieval workflows that Claude models handle natively.
All 14 Llama models are open source with 2 available completely free, enabling self-hosting and fine-tuning without API costs or vendor lock-in. Anthropic's 13 closed models require API access at $1.25-$150/M output tokens but deliver 21-point higher average performance (55 vs 34) with guaranteed uptime and no infrastructure overhead.
Anthropic provides vision across all 13 models with scores averaging 55/100, while Meta only offers vision in 4/14 models despite pricing as low as $0.040/M. For vision-heavy workloads, paying Anthropic's premium gets you 3.25x more model options and consistently higher performance scores.
Using cheapest options: Meta's Llama at $0.040/M costs $4/month while Anthropic at $1.25/M costs $125/month - a 31x difference. However, Anthropic's Claude models include reasoning (11/13) and web search (12/13) capabilities that Llama completely lacks, potentially eliminating the need for additional specialized models or custom implementations.
Both providers max out at 1M tokens, but Anthropic's models leverage this with 85% reasoning coverage (11/13) for complex document analysis, while Llama's 0/14 reasoning support limits large context use to basic retrieval. Claude Sonnet 4.6 at 66/100 can handle sophisticated long-context tasks that even Llama 4 Maverick at 54/100 cannot attempt due to missing reasoning capabilities.