GPT-4o Audio vs Qwen3-105B
GPT-4o Audio (2024) and Qwen3-105B (2025) are compact production models from OpenAI and Alibaba. GPT-4o Audio ships a 128K-token context window, while Qwen3-105B ships a 128k-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.
Qwen3-105B is safer overall; choose GPT-4o Audio when provider fit matters.
Decision scorecard
Local evidence first| Signal | GPT-4o Audio | Qwen3-105B |
|---|---|---|
| Best for | general production evaluation | tool-calling agents |
| Decision fit | Long context | RAG, Agents, and Long context |
| Context window | 128K | 128k |
| Cheapest output | $10/1M tokens | - |
| Provider routes | 1 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GPT-4o Audio has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags GPT-4o Audio for Long context.
- Qwen3-105B uniquely exposes Function calling and Tool use in local model data.
- Local decision data tags Qwen3-105B for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GPT-4o Audio
$4,500
Cheapest tracked route/tier: OpenRouter
Qwen3-105B
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for GPT-4o Audio and Qwen3-105B; plan for SDK, billing, or endpoint changes.
- Qwen3-105B adds Function calling and Tool use in local capability data.
- No overlapping tracked provider route is sourced for Qwen3-105B and GPT-4o Audio; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Function calling and Tool use before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-10-01 | 2025-12-15 |
| Context window | 128K | 128k |
| Parameters | — | 105B |
| Architecture | decoder only | - |
| License | Unknown | Open Source |
| Knowledge cutoff | 2023-10 | 2025-02 |
Pricing and availability
| Pricing attribute | GPT-4o Audio | Qwen3-105B |
|---|---|---|
| Input price | $2.50/1M tokens | - |
| Output price | $10/1M tokens | - |
| Providers | - |
Capabilities
| Capability | GPT-4o Audio | Qwen3-105B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | No | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on function calling: Qwen3-105B and tool use: Qwen3-105B. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
Pricing coverage is uneven: GPT-4o Audio has $2.50/1M input tokens and Qwen3-105B has no token price sourced yet. Provider availability is 1 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose GPT-4o Audio when provider fit and broader provider choice are central to the workload. Choose Qwen3-105B when provider fit are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, GPT-4o Audio or Qwen3-105B?
GPT-4o Audio supports 128K tokens, while Qwen3-105B supports 128k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Is GPT-4o Audio or Qwen3-105B open source?
GPT-4o Audio is listed under Unknown. Qwen3-105B is listed under Open Source. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for function calling, GPT-4o Audio or Qwen3-105B?
Qwen3-105B has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for tool use, GPT-4o Audio or Qwen3-105B?
Qwen3-105B has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run GPT-4o Audio and Qwen3-105B?
GPT-4o Audio is available on OpenRouter. Qwen3-105B is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
When should I pick GPT-4o Audio over Qwen3-105B?
Qwen3-105B is safer overall; choose GPT-4o Audio when provider fit matters. If your workload also depends on provider fit, start with GPT-4o Audio; if it depends on provider fit, run the same evaluation with Qwen3-105B.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.