LLM Reference

Aquila Chat 2 70B Expressive vs Llama Guard 4 12B

Aquila Chat 2 70B Expressive (2023) and Llama Guard 4 12B (2025) are compact production models from Beijing Academy of Artificial Intelligence (BAAI) and AI at Meta. Aquila Chat 2 70B Expressive ships a 2k-token context window, while Llama Guard 4 12B ships a 164k-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 Guard 4 12B fits 82x more tokens; pick it for long-context work and Aquila Chat 2 70B Expressive for tighter calls.

Decision scorecard

Local evidence first
SignalAquila Chat 2 70B ExpressiveLlama Guard 4 12B
Best forgeneral production evaluationprovider-routed production
Decision fitGeneralRAG, Long context, and Classification
Context window2k164k
Cheapest output-$0.18/1M tokens
Provider routes0 tracked3 tracked
Shared benchmarks0 shared0 shared

Decision tradeoffs

Choose Aquila Chat 2 70B Expressive when...
  • Use Aquila Chat 2 70B Expressive when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
Choose Llama Guard 4 12B when...
  • Llama Guard 4 12B has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • Llama Guard 4 12B has broader tracked provider coverage for fallback and procurement flexibility.
  • Llama Guard 4 12B uniquely exposes Structured outputs in local model data.
  • Local decision data tags Llama Guard 4 12B for RAG, Long context, and Classification.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output route or tier on this page.

Aquila Chat 2 70B Expressive

Unavailable

No complete token price in local provider data

Llama Guard 4 12B

$189

Cheapest tracked route/tier: OpenRouter

Cost delta unavailable until both models have sourced input and output token prices.

Switch friction

Aquila Chat 2 70B Expressive -> Llama Guard 4 12B
  • No overlapping tracked provider route is sourced for Aquila Chat 2 70B Expressive and Llama Guard 4 12B; plan for SDK, billing, or endpoint changes.
  • Llama Guard 4 12B adds Structured outputs in local capability data.
Llama Guard 4 12B -> Aquila Chat 2 70B Expressive
  • No overlapping tracked provider route is sourced for Llama Guard 4 12B and Aquila Chat 2 70B Expressive; plan for SDK, billing, or endpoint changes.
  • Check replacement coverage for Structured outputs before moving production traffic.

Specs

Specification
Released2023-11-022025-04-05
Context window2k164k
Parameters70B12B
ArchitectureDecoder OnlyDecoder Only
LicenseProprietaryLlama 2 Community
OpennessProprietaryOpen weights
Commercial useCommercial use: conditionalCommercial use: conditional
Knowledge cutoff-2024-08

Pricing and availability

Pricing attributeAquila Chat 2 70B ExpressiveLlama Guard 4 12B
Input price-$0.18/1M tokens
Output price-$0.18/1M tokens
Providers-

Capabilities

CapabilityAquila Chat 2 70B ExpressiveLlama Guard 4 12B
VisionNoNo
MultimodalNoNo
ReasoningNoNo
Function callingNoNo
Tool useNoNo
Structured outputsNoYes
Code executionNoNo
IDE integrationNoNo
Computer useNoNo
Parallel agentsNoNo

Benchmarks

No shared benchmark scores are currently available for this pair.

Deep dive

The capability footprint differs most on structured outputs: Llama Guard 4 12B. 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 Chat 2 70B Expressive has no token price sourced yet and Llama Guard 4 12B has $0.18/1M input tokens. Provider availability is 0 tracked routes versus 3. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Aquila Chat 2 70B Expressive when provider fit are central to the workload. Choose Llama Guard 4 12B 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. 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, Aquila Chat 2 70B Expressive or Llama Guard 4 12B?

Llama Guard 4 12B supports 164k tokens, while Aquila Chat 2 70B Expressive 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 Chat 2 70B Expressive or Llama Guard 4 12B open source?

Aquila Chat 2 70B Expressive is listed under Proprietary. Llama Guard 4 12B 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.

Which is better for structured outputs, Aquila Chat 2 70B Expressive or Llama Guard 4 12B?

Llama Guard 4 12B 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 Chat 2 70B Expressive and Llama Guard 4 12B?

Aquila Chat 2 70B Expressive is available on the tracked providers still being sourced. Llama Guard 4 12B is available on NVIDIA NIM, Replicate API, and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

When should I pick Aquila Chat 2 70B Expressive over Llama Guard 4 12B?

Llama Guard 4 12B fits 82x more tokens; pick it for long-context work and Aquila Chat 2 70B Expressive for tighter calls. If your workload also depends on provider fit, start with Aquila Chat 2 70B Expressive; if it depends on long-context analysis, run the same evaluation with Llama Guard 4 12B.

Continue comparing

Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.