Aquila 2 7B vs ELYZA Japanese Llama 2 13B
Aquila 2 7B (2023) and ELYZA Japanese Llama 2 13B (2023) are compact production models from Beijing Academy of Artificial Intelligence (BAAI) and ELYZA. Aquila 2 7B ships a 2k-token context window, while ELYZA Japanese Llama 2 13B ships a not-yet-sourced context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Aquila 2 7B is safer overall; choose ELYZA Japanese Llama 2 13B when provider fit matters.
Decision scorecard
Local evidence first| Signal | Aquila 2 7B | ELYZA Japanese Llama 2 13B |
|---|---|---|
| Best for | general production evaluation | general production evaluation |
| Decision fit | General | General |
| Context window | 2k | — |
| Cheapest output | - | - |
| Provider routes | 0 tracked | 0 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- Aquila 2 7B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Use ELYZA Japanese Llama 2 13B when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
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
ELYZA Japanese Llama 2 13B
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 ELYZA Japanese Llama 2 13B; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for ELYZA Japanese Llama 2 13B and Aquila 2 7B; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2023-11-02 | 2023-08-02 |
| Context window | 2k | — |
| Parameters | 7B | 13B |
| Architecture | Decoder Only | Decoder Only |
| License | Proprietary | Llama 2 Community |
| Openness | Proprietary | Open weights |
| Weights | Not released | Unknown |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: conditional | Commercial use: conditional |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Aquila 2 7B | ELYZA Japanese Llama 2 13B |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | Aquila 2 7B | ELYZA Japanese Llama 2 13B |
|---|---|---|
| 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 scores are currently available 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 ELYZA Japanese Llama 2 13B has no token price sourced yet. Provider availability is 0 tracked routes versus 0. 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 ELYZA Japanese Llama 2 13B 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
Is Aquila 2 7B or ELYZA Japanese Llama 2 13B open source?
Aquila 2 7B is listed under Proprietary. ELYZA Japanese Llama 2 13B is listed under Llama 2 Community. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
When should I pick Aquila 2 7B over ELYZA Japanese Llama 2 13B?
Aquila 2 7B is safer overall; choose ELYZA Japanese Llama 2 13B when provider fit matters. If your workload also depends on provider fit, start with Aquila 2 7B; if it depends on provider fit, run the same evaluation with ELYZA Japanese Llama 2 13B.
What is the main difference between Aquila 2 7B and ELYZA Japanese Llama 2 13B?
Aquila 2 7B and ELYZA Japanese Llama 2 13B differ most on context, provider coverage, capabilities, or pricing depending on the data currently sourced. Use the specs table first, then validate the model behavior with your own prompts.
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Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.