Llama 4 Maverick vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 4 Maverick by Meta against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Llama 4 Maverick
原生多模态视觉模型Meta 推出的 Llama 4 Maverick 是一款原生支持图文多模态理解、视觉图表解析与跨模态推理的大模型，具备 1M 上下文窗口、85 Token/秒生成速度与 84/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
1M tokens
62%
GPT-6.1 Sol
旗舰前沿大模型OpenAI 推出的 GPT-6.1 Sol 是一款面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿大模型，具备 1M 上下文窗口、120 Token/秒生成速度与 94/100 综合智能指数（每百万 Token $2 输入 / $10 输出）。
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
核心差异一览
- Llama 4 Maverick is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol is faster at 120 tok/s vs 85 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 62.0%).
| Feature | Llama 4 Maverick | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 84 | 94 |
Provider | Meta | OpenAI |
Input Price Cost per million input tokens | $0.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 33K | 128K |
Throughput Speed Tokens generated per second | 85 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 62% | 82.3% |
GPQA Diamond Graduate-level reasoning | 72.5% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.10 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 4 Maverick · Strengths & Key Advantages
- 提供 1M 上下文窗口与最高 33K 输出容量，核心基准表现优异（SWE-bench Verified 62% · GPQA Diamond 72.5%）
- 高达 85 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 550 毫秒
- 开放模型权重 (17B (400B total MoE))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
GPT-6.1 Sol · Strengths & Key Advantages
- 提供 1M 上下文窗口与最高 128K 输出容量，核心基准表现优异（SWE-bench Verified 82.3% · GPQA Diamond 80.8%）
- 高达 120 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 1120 毫秒
- 极具竞争力的 API 定价（每百万 Token $2 输入 / $10 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在大规模高频调用场景下，建议结合提示词缓存 (Prompt Caching) 与批量 API 以控制成本
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 4 Maverick | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.05 |
Output Tokens / Task Tokens generated per task | 15K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 4 Maverick | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-4-Maverick | gpt-6-1-sol |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-04 | 2026-09 |
| Knowledge Cutoff | January 2025 | April 2026 |
| Architecture | High-capacity sparse MoE multimodal transformer (17B active / 400B total) | Near-Astra unified reasoning model |
| Parameters | 17B (400B total MoE) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $0.1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 11 GB VRAM (4-bit Quantized) · 37 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Llama 4 Maverick vs GPT-6.1 Sol: 常见问题解答 (FAQ)
Which is more intelligent, Llama 4 Maverick or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 84 on the composite Intelligence Index.
Which model is faster?
GPT-6.1 Sol is faster, generating 120 tokens per second compared with 85 tokens per second.
Which model is cheaper to run?
Llama 4 Maverick is cheaper at $0.00 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 4 Maverick has lower latency, with a time to first token of 0.55s compared with 1.12s.
综合结论
Choose Llama 4 Maverick for 原生支持图文多模态理解、视觉图表解析与跨模态推理的任务、企业级 rag 检索增强生成、自动化智能体工作流与高可靠 api 集成。, or choose GPT-6.1 Sol for 面向复杂企业级推理、软件开发与全场景生产工作流的旗舰前沿任务、企业级 rag 检索增强生成、自动化智能体工作流与高可靠 api 集成。.
常见问题 (FAQ)
Which is cheaper: Llama 4 Maverick or GPT-6.1 Sol?+
Llama 4 Maverick costs $0/1M input and $0/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Llama 4 Maverick or GPT-6.1 Sol?+
Llama 4 Maverick supports 1M tokens whereas GPT-6.1 Sol supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
数据来源: Meta & OpenAI Official Documentation