Aquila 2 34B vs GPT-4
Aquila 2 34B (2023) and GPT-4 (2023) are compact production models from Beijing Academy of Artificial Intelligence (BAAI) and OpenAI. Aquila 2 34B ships a not-yet-sourced context window, while GPT-4 ships a 8K-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.
Aquila 2 34B is safer overall; choose GPT-4 when coding workflow support matters.
Decision scorecard
Local evidence first| Signal | Aquila 2 34B | GPT-4 |
|---|---|---|
| Decision fit | General | Coding, Agents, and Vision |
| Context window | — | 8K |
| Cheapest output | - | $60/1M tokens |
| Provider routes | 0 tracked | 4 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Use Aquila 2 34B when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
- GPT-4 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-4 has broader tracked provider coverage for fallback and procurement flexibility.
- GPT-4 uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags GPT-4 for Coding, Agents, and Vision.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Aquila 2 34B
Unavailable
No complete token price in local provider data
GPT-4
$39,000
Cheapest tracked route: OpenAI API
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for Aquila 2 34B and GPT-4; plan for SDK, billing, or endpoint changes.
- GPT-4 adds Vision, Multimodal, and Function calling in local capability data.
- No overlapping tracked provider route is sourced for GPT-4 and Aquila 2 34B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2023-11-02 | 2023-03-14 |
| Context window | — | 8K |
| Parameters | 34B | 1.76T (8x222B MoE)* |
| Architecture | decoder only | mixture of experts |
| License | Unknown | Proprietary |
| Knowledge cutoff | - | 2021-09 |
Pricing and availability
| Pricing attribute | Aquila 2 34B | GPT-4 |
|---|---|---|
| Input price | - | $30/1M tokens |
| Output price | - | $60/1M tokens |
| Providers | - |
Capabilities
| Capability | Aquila 2 34B | GPT-4 |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | No |
| Structured outputs | No | Yes |
| Code execution | No | Yes |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: GPT-4, multimodal input: GPT-4, function calling: GPT-4, structured outputs: GPT-4, and code execution: GPT-4. 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: Aquila 2 34B has no token price sourced yet and GPT-4 has $30/1M input tokens. Provider availability is 0 tracked routes versus 4. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Aquila 2 34B when provider fit are central to the workload. Choose GPT-4 when coding workflow support and broader provider choice 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
Is Aquila 2 34B or GPT-4 open source?
Aquila 2 34B is listed under Unknown. GPT-4 is listed under Proprietary. 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 vision, Aquila 2 34B or GPT-4?
GPT-4 has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Aquila 2 34B or GPT-4?
GPT-4 has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, Aquila 2 34B or GPT-4?
GPT-4 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 structured outputs, Aquila 2 34B or GPT-4?
GPT-4 has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Aquila 2 34B and GPT-4?
Aquila 2 34B is available on the tracked providers still being sourced. GPT-4 is available on OpenAI API, Azure OpenAI, Salesforce Einstein Generative AI, and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.