Apertus 70B
regolo-ai/apertus-70bVon regolo-ai · veröffentlicht 2025-09-02 · Wissensstand: 2025-09
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Fähigkeiten
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding41
- Tool calling40/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)0/20
- Provider availability1/10
Agents51
- Tool calling35/35
- Structured output0/25
- Reasoning0/15
- Output token limit15/15
- Provider availability1/10
JSON / structured output44
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume14/20
Cost efficiency51
- Headline price (log-scaled)51/95
- Has prompt-cache pricing0/5
Long context0
- Context ≥ 100K0/100
Production-readiness58
- Number of independent providers5/40
- Has published per-token price20/20
- Context window ≥ 8K8/15
- No data inconsistencies across providers10/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $3.51 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $7.02 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $2.13 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $6.10 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $6.97 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Preis-Details
Empfohlene Preise von regolo-ai · apertus-70b
Bei 1 Anbietern verfügbar
| Anbieter | Anbieter-Modell-ID | Eingabe / 1M | Ausgabe / 1M | Kontext | Veröffentlicht |
|---|---|---|---|---|---|
| Regolo AI regolo-ai | apertus-70b | $0.460 | $2.42 | 30K | 2025-09-02 |
Frequently asked questions
How much does Apertus 70B cost?
Apertus 70B costs $0.460 per 1M input tokens and $2.42 per 1M output tokens, sourced from regolo-ai. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Apertus 70B?
Apertus 70B has a context window of 30K tokens, with a max output of 30K tokens per reply. This is the total combined size of prompt + completion.
Does Apertus 70B support tool calling?
Yes. Apertus 70B supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does Apertus 70B support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Apertus 70B in our data source. Verify with regolo-ai's official documentation before relying on it in production.
Can Apertus 70B accept image input?
No. Apertus 70B only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Apertus 70B open-weight?
Yes. Apertus 70B's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to Apertus 70B?
If Apertus 70B doesn't fit, consider Brick Complexity Pro, Faster Whisper Large v3, Brick v1 Beta. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers are normalised into a single canonical model record and reconciled with each provider's official documentation. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
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Capability lists this model is in
Zuletzt aktualisiert:
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.