ERNIE 4.5 vs Llama 3.1 70B Instruct
ERNIE 4.5 (2025) and Llama 3.1 70B Instruct (2024) are compact production models from Baidu AI and AI at Meta. ERNIE 4.5 ships a 8K-token context window, while Llama 3.1 70B Instruct ships a 128K-token context window. On pricing, Llama 3.1 70B Instruct costs $0.4/1M input tokens versus $0.59/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.1 70B Instruct is ~47% cheaper at $0.4/1M; pay for ERNIE 4.5 only for provider fit.
Decision scorecard
Local evidence first| Signal | ERNIE 4.5 | Llama 3.1 70B Instruct |
|---|---|---|
| Decision fit | General | Coding, RAG, and Long context |
| Context window | 8K | 128K |
| Cheapest output | $2.36/1M tokens | $0.4/1M tokens |
| Provider routes | 2 tracked | 11 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Use ERNIE 4.5 when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
- Llama 3.1 70B Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 3.1 70B Instruct has the lower cheapest tracked output price at $0.4/1M tokens.
- Llama 3.1 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Llama 3.1 70B Instruct uniquely exposes Structured outputs in local model data.
- Local decision data tags Llama 3.1 70B Instruct for Coding, RAG, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
ERNIE 4.5
$1,062
Cheapest tracked route: Baidu Qianfan
Llama 3.1 70B Instruct
$420
Cheapest tracked route: Hyperbolic AI Inference
Estimated monthly gap: $642. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Llama 3.1 70B Instruct is $1.96/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Llama 3.1 70B Instruct adds Structured outputs in local capability data.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- ERNIE 4.5 is $1.96/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Structured outputs before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-03-16 | 2024-07-23 |
| Context window | 8K | 128K |
| Parameters | — | 70B |
| Architecture | decoder only | decoder only |
| License | Unknown | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | ERNIE 4.5 | Llama 3.1 70B Instruct |
|---|---|---|
| Input price | $0.59/1M tokens | $0.4/1M tokens |
| Output price | $2.36/1M tokens | $0.4/1M tokens |
| Providers |
Capabilities
| Capability | ERNIE 4.5 | Llama 3.1 70B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on structured outputs: Llama 3.1 70B Instruct. 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.
For cost, ERNIE 4.5 lists $0.59/1M input and $2.36/1M output tokens, while Llama 3.1 70B Instruct lists $0.4/1M input and $0.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.1 70B Instruct lower by about $0.72 per million blended tokens. Availability is 2 providers versus 11, so concentration risk also matters.
Choose ERNIE 4.5 when provider fit are central to the workload. Choose Llama 3.1 70B Instruct when long-context analysis, larger context windows, and lower input-token cost 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, ERNIE 4.5 or Llama 3.1 70B Instruct?
Llama 3.1 70B Instruct supports 128K tokens, while ERNIE 4.5 supports 8K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, ERNIE 4.5 or Llama 3.1 70B Instruct?
Llama 3.1 70B Instruct is cheaper on tracked token pricing. ERNIE 4.5 costs $0.59/1M input and $2.36/1M output tokens. Llama 3.1 70B Instruct costs $0.4/1M input and $0.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is ERNIE 4.5 or Llama 3.1 70B Instruct open source?
ERNIE 4.5 is listed under Unknown. Llama 3.1 70B Instruct is listed under Open Source. 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, ERNIE 4.5 or Llama 3.1 70B Instruct?
Llama 3.1 70B Instruct 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 ERNIE 4.5 and Llama 3.1 70B Instruct?
ERNIE 4.5 is available on Fireworks AI and Baidu Qianfan. Llama 3.1 70B Instruct is available on OctoAI API (Deprecated), Together AI, Fireworks AI, NVIDIA NIM, and Microsoft Foundry. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick ERNIE 4.5 over Llama 3.1 70B Instruct?
Llama 3.1 70B Instruct is ~47% cheaper at $0.4/1M; pay for ERNIE 4.5 only for provider fit. If your workload also depends on provider fit, start with ERNIE 4.5; if it depends on long-context analysis, run the same evaluation with Llama 3.1 70B Instruct.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.