Cohere
No-cost research and development inference across 10 models; not unrestricted production use.
https://api.cohere.ai/v2Models mentioned
Command A+Command A ReasoningCommand A TranslateCommand A VisionCommand ACommand R+Command RCommand R7BNorth Mini Codecommand-a-plus-05-2026Equivalent paid value
How this was valued: Best-case rate-limit envelope for the trial key's documented shared cap of 1,000 API calls per month, filled entirely with command-r-plus-08-2024 chat calls at the model's documented per-request ceilings (128K context window, 4K maximum output tokens per Cohere's models documentation) and priced at Cohere's current published rate for that exact model ($2.50 input / $10.00 output per 1M tokens, stated "for existing customers"). The 20 req/min trial chat limit never binds against the monthly cap. Daily and weekly figures are pro-rated shares of the monthly call bucket, not independent daily quotas. Assumes every call carries a maximal 128K-token request; the 1,000-call bucket is shared across all endpoints and models, so per-model rows are alternative maxima and are not additive.
Partial because the Command R+ price is scoped to existing customers on the pricing page; the priciest trial-eligible models cannot anchor the envelope (Command A+ and North Mini Code are listed by Cohere at $0 API price, and Command A Reasoning/Translate/Vision have no published token price, only "contact sales"), and embed/rerank endpoint allowances are excluded from the total.
Limits and terms
- Calls Per Month
- 1,000
- Chat RPM
- 20
- Audio Transcription RPM
- 5
- Rerank RPM
- 10
- Embed Inputs Per Minute
- 2,000
- Embed Image Inputs Per Minute
- 5
- Embed Job RPM
- 5
- Tokenize RPM
- 100
- Parse RPM
- 500
- Default Endpoint RPM
- 500
What happens to your prompts?
Content can be logged for 30 days and reviewed for abuse, while training is opt-in and aggregate improvement uses remain.
- Plan Scope
- Cohere trial API and enterprise SaaS; trial, enterprise, training opt-in, and approved zero-data-retention configurations differ.
- Prompt Retention
- Cohere says logged prompts and generations are automatically deleted after 30 days, except for legal or contractual requirements, flagged abuse, or content separately allowed for training. Approved zero-data-retention accounts do not log prompts or generations.
- Response Retention
- Same 30-day default and exceptions as prompts.
- Ordinary Logging
- Cohere logs and monitors platform use for agreement enforcement and security, and collects non-customer-identifying usage measures such as frequency, duration, features, preferences, and aggregate input-token counts.
- Model Training
- Training use is opt-in. Cohere says common personal information is filtered from opted-in prompts and generations before possible model training; separately submitted fine-tuning data is governed by its feature and agreement terms.
- Product Improvement
- Aggregate usage data may be used to understand use and improve performance. Prompt or generation training use requires opt-in, while de-identified flagged content may be aggregated to evaluate safety detection and policy enforcement.
- Human Or Operator Access
- Safety and security teams may review prompts, generations, and logs flagged as possible misuse.
- Subprocessors And Routing
- Cohere uses published subprocessors, including Google Cloud infrastructure and monitoring, delivery, support, and analytics vendors; third-party platforms integrating Cohere can impose their own handling.
- Deletion Controls
- Trial users can delete the platform account and request deletion of inadvertently submitted personal information. Enterprise data is normally deleted after 30 days, while approved ZDR purges content after processing and agreement-specific exceptions can apply.
- Caveat
- The free trial is not intended to process personal information. Zero data retention is not the default and requires Cohere approval.
Governing documents
- Trial Terms
- https://cohere.com/terms-of-use
- Saas Agreement
- https://cohere.com/saas-agreement
- Privacy Policy
- https://cohere.com/privacy
Eligibility
- Account Required
- Yes
- Payment Method Required
- No
- Allowed Use
- evaluation and prototyping
Primary sources
Before you build with Cohere
Read the classification narrowly
This record has current first-party evidence for some zero-cost hosted inference. The label describes the bounded offer supported by evidence on 2026-09-19; it does not guarantee permanence, production suitability, uptime, latency, model quality, or access from every region.
Resolve the live model route
This snapshot records 10 model IDs. Match the exact ID against the provider's live catalog before using it in code; zero-price routes and aliases can rotate while an older documentation page remains online. The recorded base endpoint is https://api.cohere.ai/v2, but the provider's current API reference remains authoritative for paths, authentication, and request shape.
Confirm account and billing boundaries
An account is required, so public catalog evidence cannot by itself prove the limits, balance, or billing behavior attached to your workspace. The reviewed evidence explicitly says no payment card is required. Re-check the signup flow because eligibility and billing controls can change after the snapshot. The equivalent paid value shown here prices a documented allowance where possible; it is not cash, guaranteed savings, or protection from overage.
Re-read the prompt policy for your plan
This record classifies the reviewed handling as “Partially private”: Content can be logged for 30 days and reviewed for abuse, while training is opt-in and aggregate improvement uses remain. Provider policies can distinguish free, paid, enterprise, opted-in, and feature-specific traffic, so verify the governing terms for the exact account and route that will receive your data.
Follow the evidence, then re-check it
The record links 11 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest governing document or live catalog when sources disagree, and submit a correction when a provider changes a material term.
Match the quota to the workload shape
Translate the published allowance into the traffic pattern you actually expect instead of comparing headline totals alone. A daily token pool can look generous while a low requests-per-minute or concurrency ceiling blocks interactive bursts; a high request limit can still fail a long-context job when input, output, or per-request tokens are capped. Separate prompt tokens from generated tokens, include retries and tool calls, and test the largest realistic payload. If the provider documents more than one limit window, the tightest window at your peak load is the practical ceiling. Research, experimental, and community access can also carry eligibility or fair-use constraints that cannot be modeled as a simple number.
Plan fallback without changing the rules
A fallback should preserve more than API syntax. Confirm that the substitute route supports the required modality, context length, streaming behavior, structured output, tools, and safety controls, then compare its prompt-retention and training terms. An OpenAI-compatible request shape does not make providers operationally or contractually equivalent. Decide which errors may trigger a retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. If deterministic output matters, record the model revision and sampling settings; rotating aliases and free-model pools can change behavior even when the endpoint remains available.
Monitor the offer as a dependency
Capture the model ID, response model field, rate-limit headers, usage fields, latency, HTTP status, and any provider request identifier for each test. Watch for authorization failures, quota exhaustion, catalog removal, policy revisions, and dashboard balance changes as separate failure modes. Re-check the provider’s live catalog and governing pages on a schedule proportionate to the workload’s importance, and keep the dated sources that supported your decision. Free capacity is especially suitable for prototypes, evaluations, fallbacks, and bounded workloads when the application can tolerate change; a production dependency still needs observability, an exit path, and an owner responsible for re-verification.
Test one complete request before scaling
Start with the smallest permitted request using the exact credential, model ID, endpoint, and account type you intend to deploy. Record the HTTP status, response headers, usage fields, latency, and any dashboard balance change. Then exercise the failure path: an invalid model, an exhausted quota, or a rate-limit response should fail clearly without silently switching to a paid route. If the provider supports streaming or tool calls, validate those features separately because a free model can expose a narrower capability set than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and avoid sending sensitive data until the observed route matches the reviewed data agreement.