GPT-6.1 Sol vs Llama 4 Behemoth
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-6.1 Sol by OpenAI against Llama 4 Behemoth by Meta across real-world coding, reasoning, token economics, and latency.
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GPT-6.1 Sol
Near-Astra ValueDelivers results close to GPT-6 Astra at roughly one fifth of the price — the latest 2026 release.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Llama 4 Behemoth
2T Open GiantMeta's gargantuan 2-Trillion parameter open model pushing the frontier of open-weight capability and 10M context.
$0.00 / 1M
$0.00 / 1M
10M tokens
75%
Key Differences at a Glance
- Llama 4 Behemoth is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 4 Behemoth has a larger context window (10M vs 1M tokens).
- GPT-6.1 Sol is faster at 120 tok/s vs 45 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 75.0%).
| Feature | GPT-6.1 Sol | Llama 4 Behemoth |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 94 | 93 |
Provider | OpenAI | Meta |
Input Price Cost per million input tokens | $2.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.47 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 10M tokens~15,000 A4 pages of text |
Max Output Tokens Per single request | 128K | 64K |
Throughput Speed Tokens generated per second | 120 tok/s | 45 tok/s |
SWE-bench Verified Real-world software engineering | 82.3% | 75% |
GPQA Diamond Graduate-level reasoning | 80.8% | 82% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.00 / 1M |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-6.1 Sol · Strengths & Key Advantages
- Near-Astra results at roughly a fifth of the cost
- Excellent coding and long-horizon agent performance
- Strong reliability with far lower latency than Astra
- Near-flagship results at roughly a fifth of the price
Considerations & Trade-offs
- Still marginally behind Astra on the very hardest reasoning
- Same price as GPT-6 Sol, so the upgrade decision is easy
Llama 4 Behemoth · Strengths & Key Advantages
- Unrivaled 2T parameter open capacity
- Staggering ~10 Million token context horizon
- State-of-the-art coding and reasoning scores
Considerations & Trade-offs
- Demands high-end datacenter multi-node GPU clusters (8x H100 minimum)
- Slow inference throughput compared to smaller models
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-6.1 Sol | Llama 4 Behemoth |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.05 | $0.00 |
Output Tokens / Task Tokens generated per task | 62K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 36K | 15K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-6.1 Sol | Llama 4 Behemoth |
|---|---|---|
| Official API Model ID (SDK) | gpt-6-1-sol | meta-llama/Llama-4-Behemoth |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2026-05 |
| Knowledge Cutoff | April 2026 | February 2026 |
| Architecture | Near-Astra unified reasoning model | Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) |
| Parameters | Confidential | 2T total / 288B active (16-of-2 MoE) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 1000K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.1/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| 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 | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1240 GB VRAM (4-bit Quantized) · 4300 GB VRAM (Native BF16) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
GPT-6.1 Sol vs Llama 4 Behemoth: Quick Answers
Which is more intelligent, GPT-6.1 Sol or Llama 4 Behemoth?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 93 on the composite Intelligence Index.
Which model is faster?
GPT-6.1 Sol is faster, generating 120 tokens per second compared with 45 tokens per second.
Which model is cheaper to run?
Llama 4 Behemoth 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?
GPT-6.1 Sol has lower latency, with a time to first token of 1.12s compared with 1.20s.
Which model has a larger context window?
Llama 4 Behemoth has a larger context window, supporting 10M tokens compared with 1M tokens.
Verdict
Choose GPT-6.1 Sol for the best value frontier model for professional and agentic work, or choose Llama 4 Behemoth for national laboratories, large enterprise ai clusters, and extreme scientific modeling.
Frequently asked questions
Which is cheaper: GPT-6.1 Sol or Llama 4 Behemoth?+
GPT-6.1 Sol costs $2/1M input and $10/1M output tokens, while Llama 4 Behemoth costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: GPT-6.1 Sol or Llama 4 Behemoth?+
GPT-6.1 Sol supports 1M tokens whereas Llama 4 Behemoth supports 10M 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.
Sources: OpenAI & Meta Official Documentation