ELYZA Japanese Llama 2 7B vs ShieldGemma 9B
ELYZA Japanese Llama 2 7B (2023) and ShieldGemma 9B (2024) are compact production models from ELYZA and Google DeepMind. ELYZA Japanese Llama 2 7B ships a not-yet-sourced context window, while ShieldGemma 9B ships a 8k-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. It focuses on practical selection signals rather than broad model-family marketing.
ShieldGemma 9B is safer overall; choose ELYZA Japanese Llama 2 7B when provider fit matters.
Decision scorecard
Local evidence first| Signal | ELYZA Japanese Llama 2 7B | ShieldGemma 9B |
|---|---|---|
| Best for | provider-routed production | general production evaluation |
| Decision fit | General | Classification |
| Context window | — | 8k |
| Cheapest output | $0.20/1M tokens | - |
| Provider routes | 2 tracked | 1 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- ELYZA Japanese Llama 2 7B has broader tracked provider coverage for fallback and procurement flexibility.
- ShieldGemma 9B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags ShieldGemma 9B for Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
ELYZA Japanese Llama 2 7B
$210
Cheapest tracked route/tier: Fireworks AI
ShieldGemma 9B
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 ELYZA Japanese Llama 2 7B and ShieldGemma 9B; plan for SDK, billing, or endpoint changes.
- No overlapping tracked provider route is sourced for ShieldGemma 9B and ELYZA Japanese Llama 2 7B; plan for SDK, billing, or endpoint changes.
Specs
| Specification | ||
|---|---|---|
| Released | 2023-08-02 | 2024-07-01 |
| Context window | — | 8k |
| Parameters | 7B | 9B |
| Architecture | Decoder Only | Decoder Only |
| License | Llama 2 Community | Gemma |
| Openness | Open weights | Open weights |
| Commercial use | Commercial use: conditional | Commercial use: conditional |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | ELYZA Japanese Llama 2 7B | ShieldGemma 9B |
|---|---|---|
| Input price | $0.20/1M tokens | - |
| Output price | $0.20/1M tokens | - |
| Providers |
Capabilities
| Capability | ELYZA Japanese Llama 2 7B | ShieldGemma 9B |
|---|---|---|
| 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: ELYZA Japanese Llama 2 7B has $0.20/1M input tokens and ShieldGemma 9B has no token price sourced yet. Provider availability is 2 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose ELYZA Japanese Llama 2 7B when provider fit and broader provider choice are central to the workload. Choose ShieldGemma 9B 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 ELYZA Japanese Llama 2 7B or ShieldGemma 9B open source?
ELYZA Japanese Llama 2 7B is listed under Llama 2 Community. ShieldGemma 9B is listed under Gemma. 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 ELYZA Japanese Llama 2 7B and ShieldGemma 9B?
ELYZA Japanese Llama 2 7B is available on Fireworks AI and IBM watsonx. ShieldGemma 9B is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick ELYZA Japanese Llama 2 7B over ShieldGemma 9B?
ShieldGemma 9B is safer overall; choose ELYZA Japanese Llama 2 7B when provider fit matters. If your workload also depends on provider fit, start with ELYZA Japanese Llama 2 7B; if it depends on provider fit, run the same evaluation with ShieldGemma 9B.
What is the main difference between ELYZA Japanese Llama 2 7B and ShieldGemma 9B?
ELYZA Japanese Llama 2 7B and ShieldGemma 9B 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.
Continue comparing
Last reviewed: 2026-05-19. Data sourced from public model cards and provider documentation.