Claude Sonnet 3.5 v2 vs Llama 4 Maverick 17B Instruct
Llama 4 Maverick 17B Instruct comes out ahead, 58 to 44 on our weighted score, and it is the cheaper option too.
AnthropicClaude Sonnet 3.5 v2
44/100- ECI133.5
- Price$3.00 / $15.00
- Context200K
- Our pick
MetaLlama 4 Maverick 17B Instruct
58/100- ECI132.2
- Price$0.321 / $0.91
- Context1M
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Llama 4 Maverick 17B Instruct is our pick
Llama 4 Maverick 17B Instruct is the better all-round choice, scoring 58/100 against Claude Sonnet 3.5 v2 (44). It leads on price and context window. Claude Sonnet 3.5 v2 wins on capability and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityClaude Sonnet 3.5 v2Capabilities Index (ECI): Claude Sonnet 3.5 v2 133.5 · Llama 4 Maverick 17B Instruct 132.2
- Lowest priceLlama 4 Maverick 17B InstructLlama 4 Maverick 17B Instruct $0.468 · Claude Sonnet 3.5 v2 $6.00 per 1M tokens (3:1 blend)
- Longest contextLlama 4 Maverick 17B InstructLlama 4 Maverick 17B Instruct 1,000,000 · Claude Sonnet 3.5 v2 200,000 tokens
- Widest inputsClaude Sonnet 3.5 v2Claude Sonnet 3.5 v2: Text, Images, PDFs · Llama 4 Maverick 17B Instruct: Text, Images
- Self-hostingLlama 4 Maverick 17B InstructPublishes downloadable weights
| Measure | Weight | Claude Sonnet 3.5 v2 | Llama 4 Maverick 17B Instruct |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 57 | 56 |
| Price | 25% | 13 | 66 |
| Inputs & features | 15% | 60 | 50 |
| Context window | 10% | 32 | 60 |
| Overall | 100% | 44/100 | 58/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Claude Sonnet 3.5 v2Anthropic | Llama 4 Maverick 17B InstructMeta |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 133.5 (best) | 132.2 |
| ECI rank | #119 of 148 (best) | #122 of 148 |
| GPQA DiamondGraduate-level science questions | 55.3% | 67.0% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 8.5% | 20.6% (best) |
| Price per million tokens | ||
| Input | $3.00 | $0.321 (best) |
| Output | $15.00 | $0.91 (best) |
| Cached input | — | — |
| Blended (3:1) | $6.00 | $0.468 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 6 providers |
| Limits | ||
| Context window | 200,000 tokens | 1,000,000 tokens (best) |
| Max output | 8,192 tokens | 16,384 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | Yes | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Proprietary | Open |
| API model ID | — | — |
| API providers | 1 | 6 (best) |
| Released | Oct 22, 2024 | Apr 5, 2025 |
| Knowledge cutoff | Apr 30, 2024 | Aug 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Claude Sonnet 3.5 v2$60.00
- Llama 4 Maverick 17B Instruct$5.03
Which should you choose?
Which is better: Claude Sonnet 3.5 v2 or Llama 4 Maverick 17B Instruct?
Llama 4 Maverick 17B Instruct is the better all-round choice, scoring 58/100 against Claude Sonnet 3.5 v2 (44). It leads on price and context window. Claude Sonnet 3.5 v2 wins on capability and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Claude Sonnet 3.5 v2 or Llama 4 Maverick 17B Instruct?
Llama 4 Maverick 17B Instruct is cheaper at $0.321 input / $0.91 output per million tokens (median across 6 API providers). Claude Sonnet 3.5 v2 costs $3.00 input / $15.00 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.468 per million tokens for Llama 4 Maverick 17B Instruct versus $6.00 for Claude Sonnet 3.5 v2 (13× as much).
Which scores higher on benchmarks?
Claude Sonnet 3.5 v2 scores higher on the Capabilities Index (ECI): Claude Sonnet 3.5 v2 133.5 (#119 of 148) and Llama 4 Maverick 17B Instruct 132.2 (#122 of 148). The confidence ranges of the top two overlap (129.2–137.5 vs 128.0–134.1), so treat the gap as small. On individual benchmarks: GPQA Diamond — Llama 4 Maverick 17B Instruct 67.0%, Claude Sonnet 3.5 v2 55.3%; OTIS Mock AIME 2024–2025 — Llama 4 Maverick 17B Instruct 20.6%, Claude Sonnet 3.5 v2 8.5%.
Which is better for coding?
There are no published SWE-bench Verified results for Claude Sonnet 3.5 v2 and Llama 4 Maverick 17B Instruct yet, so there is no like-for-like coding score. On overall capability, Claude Sonnet 3.5 v2 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.
Which has the bigger context window?
Llama 4 Maverick 17B Instruct has the largest context window at 1,000,000 tokens, against 200,000 for Claude Sonnet 3.5 v2. Maximum output per response: Claude Sonnet 3.5 v2 up to 8,192, Llama 4 Maverick 17B Instruct up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Claude Sonnet 3.5 v2 accepts text, images and PDFs; Llama 4 Maverick 17B Instruct accepts text and images. Claude Sonnet 3.5 v2 handles the widest range of inputs.
Are any of these open source?
Llama 4 Maverick 17B Instruct publishes its weights and can be self-hosted; Claude Sonnet 3.5 v2 is proprietary.
Which is newer?
Llama 4 Maverick 17B Instruct is the newest, released Apr 5, 2025. Claude Sonnet 3.5 v2 came out Oct 22, 2024. Knowledge cutoff: Claude Sonnet 3.5 v2 Apr 30, 2024, Llama 4 Maverick 17B Instruct Aug 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
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