Openchat 3.5 (1210)
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Openchat 3.5 (1210)

Version 3.5 of OpenChat project released on December 10th. As open-source conversation model, specifically optimized for conversation quality and user experience. Features good conversation coherence and instruction following capabilities, providing high-quality conversational AI choice for open-source community, suitable for developers and researchers needing custom conversation systems, supporting various conversation application development and deployment.
Intelligence(Weak)
Speed(Slow)
Input Supported Modalities
Yes
Is Reasoning Model
8,192
Context Window
-
Maximum Output Tokens
-
Knowledge Cutoff

Pricing

- /M tokens
Input
- /M tokens
Output
- /M tokens
Blended Price

Quick Simple Comparison

Input

Output

OpenChat 3.5 (1210)

Basic Parameters

OpenChat 3.5 (1210)Technical Parameters
Parameter Count
Not Announced
Context Length
8,192 tokens
Training Data Cutoff
Open Source Category
Open Weights (Permissive License)
Multimodal Support
Text Only
Throughput
Release Date
2023-12-18
Response Speed
0 tokens/s

Benchmark Scores

Below is the performance of OpenChat 3.5 (1210) in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
19.77
Large Language Model Intelligence Level
Coding Index
-
Indicator of AI model performance on coding tasks
Math Index
15.37
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
31
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
23
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
4.8
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
11.5
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
67.7
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
30.7
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)
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