Aquila 2 7B vs Llama 3.2 NV EmbedQA 1B v2
Aquila 2 7B (2023) and Llama 3.2 NV EmbedQA 1B v2 (2025) are compact production models from Beijing Academy of Artificial Intelligence (BAAI) and NVIDIA AI. Aquila 2 7B ships a 2k-token context window, while Llama 3.2 NV EmbedQA 1B v2 ships a 4k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 3.2 NV EmbedQA 1B v2 is safer overall; choose Aquila 2 7B when provider fit matters.
Decision scorecard
Local evidence first| Signal | Aquila 2 7B | Llama 3.2 NV EmbedQA 1B v2 |
|---|---|---|
| Best for | general production evaluation | general production evaluation |
| Decision fit | General | General |
| Context window | 2k | 4k |
| Cheapest output | - | - |
| Provider routes | 0 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Use Aquila 2 7B when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
- Llama 3.2 NV EmbedQA 1B v2 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.2 NV EmbedQA 1B v2 has broader tracked provider coverage for fallback and procurement flexibility.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Aquila 2 7B
Unavailable
No complete token price in local provider data
Llama 3.2 NV EmbedQA 1B v2
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 Aquila 2 7B and Llama 3.2 NV EmbedQA 1B v2; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for Llama 3.2 NV EmbedQA 1B v2 and Aquila 2 7B; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2023-11-02 | 2025-03-01 |
| Context window | 2k | 4k |
| Parameters | 7B | 1B |
| Architecture | decoder only | encoder |
| License | Unknown | 1 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Aquila 2 7B | Llama 3.2 NV EmbedQA 1B v2 |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | Aquila 2 7B | Llama 3.2 NV EmbedQA 1B v2 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| 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 is close: both models cover the core production surface. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
Pricing coverage is uneven: Aquila 2 7B has no token price sourced yet and Llama 3.2 NV EmbedQA 1B v2 has no token price sourced yet. Provider availability is 0 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Aquila 2 7B when provider fit are central to the workload. Choose Llama 3.2 NV EmbedQA 1B v2 when long-context analysis, larger context windows, 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.
FAQ
Which has a larger context window, Aquila 2 7B or Llama 3.2 NV EmbedQA 1B v2?
Llama 3.2 NV EmbedQA 1B v2 supports 4k tokens, while Aquila 2 7B supports 2k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Aquila 2 7B or Llama 3.2 NV EmbedQA 1B v2 open source?
Aquila 2 7B is listed under Unknown. Llama 3.2 NV EmbedQA 1B v2 is listed under 1. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Where can I run Aquila 2 7B and Llama 3.2 NV EmbedQA 1B v2?
Aquila 2 7B is available on the tracked providers still being sourced. Llama 3.2 NV EmbedQA 1B v2 is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Aquila 2 7B over Llama 3.2 NV EmbedQA 1B v2?
Llama 3.2 NV EmbedQA 1B v2 is safer overall; choose Aquila 2 7B when provider fit matters. If your workload also depends on provider fit, start with Aquila 2 7B; if it depends on long-context analysis, run the same evaluation with Llama 3.2 NV EmbedQA 1B v2.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.