Qwen3 8B vs OpenChat 3.5 (0106)
Compare the listed model data for Qwen3 8B by Qwen and OpenChat 3.5 (0106) by openchat. Benchmark and pricing records are not a controlled test and may change.
Data last updated: 2026-10-16. Verify current model details with each provider.
What the listed data says
Qwen3 8B: 128K tokens; OpenChat 3.5 (0106): 8K tokens. Qwen3 8B has the larger listed context window. Qwen3 8B is listed for coding, reasoning, long-context tasks; OpenChat 3.5 (0106) is listed for no specific capability flags. Shared catalog benchmark entries: MMLU: Qwen3 8B 87.5%; OpenChat 3.5 (0106) 65.3%; HumanEval: Qwen3 8B 85.6%; OpenChat 3.5 (0106) 71.4%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
Catalog source links
Use these model and provider links to verify current specifications, pricing, and availability.
Computed differences from catalog data
- The listed context windows are 128K tokens and 8K tokens; the larger is 16× the smaller.
- On shared numeric benchmark entries, MMLU: Qwen3 8B − OpenChat 3.5 (0106) = +22.2 percentage points (87.5% vs 65.3%); HumanEval: Qwen3 8B − OpenChat 3.5 (0106) = +14.2 percentage points (85.6% vs 71.4%).
- The first listed parameter quantities are 8B and 7B; the larger quantity is 1.14× the smaller. Total and active MoE parameters are not directly equivalent.
- The catalog release dates are April 2025 and January 2024, about 15 months apart at the precision supplied.
Cost example: 1M input and 1M output tokens
A cost example for 1M input plus 1M output tokens cannot be calculated reliably because separate numeric input and output rates are not listed for both models.
Choose Qwen3 8B if…
Choose Qwen3 8B when your task aligns with catalog-listed uses or strengths such as Fast everyday coding assistance in Python, JS, and TypeScript, Multilingual chatbots targeting Asian and African markets, Edge deployment and cost-sensitive production APIs, Student homework help across 119 languages.
Choose OpenChat 3.5 (0106) if…
Choose OpenChat 3.5 (0106) when your task aligns with catalog-listed uses or strengths such as Legacy OpenChat 3.5 deployments, Research on efficient instruction tuning, 7.8 MT-Bench — first 7B to match ChatGPT-3.5 on MT-Bench at January 2024 release, C-RLFT training: outperforms standard RLHF without proprietary preference data.
Qwen3 8B
Compact Qwen3 powerhouse — everyday coding, multilingual analysis, and step-by-step reasoning at fast inference speeds. Uses hybrid think...
Parameters: 8B
Context: 128K tokens
Catalog pricing: Open source — free to download. API access via third-party providers ~$0.05-0.15/MTok
OpenChat 3.5 (0106)
Community-trained OpenChat 3.5 — outperforms ChatGPT-3.5 on MT-Bench at just 7B params. Friendly, conversational tone.
Parameters: 7B
Context: 8K tokens
Catalog pricing: MIT — free
Feature Comparison — Qwen3 8B vs OpenChat 3.5 (0106)
| Specification | Qwen3 8B | OpenChat 3.5 (0106) |
|---|---|---|
| Provider | Qwen | openchat |
| Parameters | 8B | 7B |
| Context window | 128K tokens | 8K tokens |
| Maximum output | Not listed | Not listed |
| Catalog pricing | Open source — free to download. API access via third-party providers ~$0.05-0.15/MTok | MIT — free |
| Listed input/output cost per 1M tokens | Separate numeric input/output rates not listed | Separate numeric input/output rates not listed |
| License | Apache 2.0 | MIT |
| Coding support | Listed | Not listed |
| Reasoning support | Listed | Not listed |
| Vision input | Not listed | Not listed |
| Multimodal | Not listed | Not listed |
| Open weights | Yes | Yes |
| Benchmark: MMLU | 87.5% | 65.3% |
| Benchmark: HumanEval | 85.6% | 71.4% |
Figures are transcribed from LITHOVEX model records. Benchmark versions and evaluation setups may differ; pricing and specifications can change. Check each provider's current documentation before making a decision.
Frequently Asked Questions
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Explore these models and related comparisons
Read the Qwen3 8B model profile and OpenChat 3.5 (0106) model profile, or browse the model directory.