
R1 1776
Reasoning-focused model with patriotic naming referencing American independence year, potentially designed for complex problem-solving tasks. Though technical details are limited, naming suggests unique design philosophy or specific application direction. As reasoning model, expected to have special advantages in logical analysis, mathematical reasoning, and complex problem-solving, suitable for applications requiring deep thinking and systematic analysis.
Intelligence(Relatively Weak)
Speed(Slow)
Input Supported Modalities
No
Is Reasoning Model
128,000
Context Window
131,072
Maximum Output Tokens
-
Knowledge Cutoff
Pricing
- /M tokens
Input
- /M tokens
Output
¥25.2 /M tokens
Blended Price
Quick Simple Comparison
Sonar Pro
R1 1776
Sonar Reasoning
Basic Parameters
R1 1776Technical Parameters
Parameter Count
671,000.0M
Context Length
128.00k tokens
Training Data Cutoff
Open Source Category
Open Weights (Permissive License)
Multimodal Support
Text Only
Throughput
Release Date
2025-02-18
Response Speed
0 tokens/s
Benchmark Scores
Below is the performance of R1 1776 in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
33.65
Large Language Model Intelligence Level
Coding Index
-
Indicator of AI model performance on coding tasks
Math Index
-
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
-
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
73.3
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
-
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
50
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
-
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
-
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
95.4
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
-
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
GPT 5 Mini
openai

¥1.8
Input tokens/million
¥14.4
Output tokens/million
400k
Context Length
GPT 5 Standard
openai

¥63
Input tokens/million
¥504
Output tokens/million
400k
Context Length
GPT 5 Nano
openai

¥0.36
Input tokens/million
¥2.88
Output tokens/million
400k
Context Length
GPT 5
openai

¥9
Input tokens/million
¥72
Output tokens/million
400k
Context Length
GLM 4.5
chatglm

¥0.43
Input tokens/million
¥1.01
Output tokens/million
131k
Context Length
Gemini 2.0 Flash Lite (Preview)
google

¥0.58
Input tokens/million
¥2.16
Output tokens/million
1M
Context Length
Gemini 1.0 Pro
google

¥3.6
Input tokens/million
¥10.8
Output tokens/million
33k
Context Length
Qwen2.5 Coder Instruct 32B
alibaba

¥0.65
Input tokens/million
¥0.65
Output tokens/million
131k
Context Length