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GPT-5.5 vs R1 Distill Llama 70B

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for GPT-5.5 and R1 Distill Llama 70B.

OpenAI

GPT-5.5

GPT-5.5 is OpenAI’s frontier model designed for complex professional workloads, building on GPT-5.4 with stronger reasoning, higher reliability, and improved token efficiency on hard tasks. It features a 1M+ token...

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DeepSeek

R1 Distill Llama 70B

DeepSeek R1 Distill Llama 70B is a distilled large language model based on [Llama-3.3-70B-Instruct](/meta-llama/llama-3.3-70b-instruct), using outputs from [DeepSeek R1](/deepseek/deepseek-r1). The model combines advanced distillation techniques to achieve high performance across...

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Technical Specifications

SpecificationGPT-5.5R1 Distill Llama 70B
ProviderOpenAIDeepSeek
Context Window1,050,000 tokens128,000 tokens
Agent Suitability95/100N/A
Time to First Token (TTFT)380 msN/A
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-04-242025-01-23

API Pricing Comparison

Input Price per Million Tokens

GPT-5.5

$5.00

R1 Distill Llama 70B

$0.80

Output Price per Million Tokens

GPT-5.5

$30.00

R1 Distill Llama 70B

$0.80

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
9420.0%vs8520.0%
GPT-5.5
R1 Distill Llama 70B
HumanEvalPython coding & logic synthesis
9680.0%vs8830.0%
GPT-5.5
R1 Distill Llama 70B
MATHComplex mathematical problem solving
9350.0%vs7000.0%
GPT-5.5
R1 Distill Llama 70B
GPQAGraduate-level expert reasoning
8420.0%vs4450.0%
GPT-5.5
R1 Distill Llama 70B
HellaSwagCommonsense reasoning and inference
9900.0%vs8600.0%
GPT-5.5
R1 Distill Llama 70B
MT-BenchMulti-turn conversation flow quality
970.0%vs905.0%
GPT-5.5
R1 Distill Llama 70B

GPT-5.5 Quirks & Gotchas

  • Best for JSON schema adherence — strict mode available via response_format parameter
  • Requires explicit tool_choice for deterministic function calling

R1 Distill Llama 70B Quirks & Gotchas

No developer gotchas reported.