💻 Coding (SWE) WinnerGPT-6.1 Sol 86.4% SWE-bench · 96.8% HumanEval
🧠 STEM Reasoning WinnerGPT-6.1 Sol 92.4% GPQA · 98.8% MATH-500
Which model is better: GPT-6.1 Sol or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct is 94.0% cheaper for input tokens ($0.12 vs. $2.00 per 1M tokens) and $0.30 vs. $10.00 for output tokens (16.7x cost advantage). In terms of operational performance, Llama 3.3 70B Instruct delivers lower response latency at 145ms TTFT (375ms faster than GPT-6.1 Sol) with 165 vs. 135 tok/s throughput. GPT-6.1 Sol leads coding evaluations at 86.4% SWE-bench Verified vs. 42.5% for Llama 3.3 70B Instruct and 92.4% vs. 55.6% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.
Zero-Shot RAG Verified
Verified Performance & Unit Economics Delta (12 Dimensions)
Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.
Metric / Dimension
GPT-6.1 Sol
Llama 3.3 70B Instruct
Net Delta / Advantage
Input Cost / 1M Tokens
$2.00
$0.12
Llama 3.3 70B Instruct is 16.7x cheaper
Output Cost / 1M Tokens
$10.00
$0.30
Llama 3.3 70B Instruct (97.0% lower)
Prompt Cache Read / 1M
$0.100
$0.030
Llama 3.3 70B Instruct is 3.3x cheaper
Prompt Cache Write / 1M
$2.00
$0.12
Llama 3.3 70B Instruct is 16.7x cheaper
Context Window (Max Output)
1,075k (128k out)
131k (33k out)
GPT-6.1 Sol (8.2x larger context)
Avg TTFT Response Latency
520ms
145ms
Llama 3.3 70B Instruct (375ms faster)
Generation Speed (Throughput)
135 tok/s
165 tok/s
Llama 3.3 70B Instruct (+30 tok/s faster)
SWE-bench Verified (Coding)
86.4%
42.5%
GPT-6.1 Sol (+43.9% lead)
GPQA Diamond (PhD STEM)
92.4%
55.6%
GPT-6.1 Sol (+36.8% lead)
MMLU-Pro (Multi-Discipline)
91%
76.8%
GPT-6.1 Sol (+14.2% lead)
MATH-500 (Quantitative Logic)
98.8%
77%
GPT-6.1 Sol (+21.8% lead)
Inference Value Score (IVR)
62.9 / 100
74.6 / 100
Llama 3.3 70B Instruct (+11.7 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose GPT-6.1 Sol (OpenAI) for delivers near-GPT-6 Astra frontier accuracy at one-fifth the token cost with a 95% cache read discount for high-efficiency agentic coding, computer use, and enterprise workflow automation — offering a 1,075k context window, 520ms TTFT, and $2.00/1M input pricing ($0.100/1M cached reads).
Which is cheaper: GPT-6.1 Sol or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct is cheaper at $0.12/1M input tokens ($0.30/1M output) compared to $2.00/1M input ($10.00/1M output) for GPT-6.1 Sol.
Which model has lower TTFT latency and higher tok/s throughput: GPT-6.1 Sol or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct delivers faster first-token latency at 145ms TTFT (165 tok/s throughput) compared to 520ms TTFT (135 tok/s) for GPT-6.1 Sol.
How do GPT-6.1 Sol and Llama 3.3 70B Instruct compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GPT-6.1 Sol scores 86.4% versus 42.5% for Llama 3.3 70B Instruct. On GPQA Diamond graduate-level STEM reasoning, GPT-6.1 Sol scores 92.4% versus 55.6% for Llama 3.3 70B Instruct (and 98.8% vs 77% on MATH-500).
When should you choose GPT-6.1 Sol?
Choose GPT-6.1 Sol (OpenAI) for delivers near-GPT-6 Astra frontier accuracy at one-fifth the token cost with a 95% cache read discount for high-efficiency agentic coding, computer use, and enterprise workflow automation — offering a 1,075k context window, 520ms TTFT, and $2.00/1M input pricing ($0.100/1M cached reads).
When should you choose Llama 3.3 70B Instruct?
Choose Llama 3.3 70B Instruct (Meta) for cost-optimized on-premise deployments, fast text transformation, structured entity extraction, and enterprise dialogue pipelines — offering a 131k context window, 145ms TTFT, and $0.12/1M input pricing ($0.030/1M cached reads).
