AI API Rate Limits 2026: OpenAI vs Claude vs Gemini vs Grok — Full Comparison
February 2026 comparison of AI API rate limits. Gemini offers 4M TPM, OpenAI 800K, Claude 80K, Grok 100K tokens per minute. Tier systems, free tiers, and how to maximize throughput.
Rate limits are the second most important factor — after price — when choosing an AI API provider. You can find the cheapest model on the market, but if its rate limits throttle you at 50 requests per minute, your production application grinds to a halt the moment real traffic arrives. Rate limits determine how many requests you can send per minute, how many tokens you can process per minute, and ultimately whether your application can scale.
The problem is that every provider structures rate limits differently. OpenAI uses a five-tier system based on cumulative spend. Anthropic uses a four-tier system. Google offers a free tier with surprisingly generous limits. xAI gives you $25 in free credits. DeepSeek keeps things flat and simple. Mistral bundles everything under a free-to-start plan.
This guide compares every major AI API provider’s rate limits side by side, breaks down which provider wins at each usage level, and shows you exactly how to architect your application to maximize throughput without hitting 429 errors.
Key Terms
Before diving into the numbers, here are the three metrics every provider uses:
- RPM (Requests Per Minute) — The maximum number of API calls you can make in a 60-second window. Each call counts as one request regardless of how many tokens it contains.
- TPM (Tokens Per Minute) — The maximum number of tokens (input + output combined) the API will process for you in a 60-second window. This is typically the binding constraint for applications with long prompts or large outputs.
- RPD (Requests Per Day) — Some providers also cap total daily requests, especially on free tiers. Less common on paid tiers.
In practice, TPM is the limit that matters most for production applications. A chatbot sending 500 short messages per minute will hit RPM limits first. A document processing pipeline sending 10 requests with 100K tokens each will hit TPM limits first. You need to understand both.
Rate Limits by Provider
OpenAI — Tier-Based System (Free through Tier 5)
OpenAI’s rate limits scale with your cumulative platform spend. The more you have paid over your account’s lifetime, the higher your tier and limits. Tiers upgrade automatically.
GPT-5 Rate Limits:
| Tier | Spend Required | RPM | TPM | RPD |
|---|---|---|---|---|
| Free | $0 | 3 | 40,000 | 200 |
| Tier 1 | $5+ | 500 | 200,000 | — |
| Tier 2 | $50+ | 5,000 | 2,000,000 | — |
| Tier 3 | $100+ | 5,000 | 4,000,000 | — |
| Tier 4 | $250+ | 10,000 | 10,000,000 | — |
| Tier 5 | $1,000+ | 10,000 | 30,000,000 | — |
GPT-4.1 and GPT-4o Rate Limits (Tier 1):
| Model | RPM | TPM |
|---|---|---|
| GPT-4.1 | 500 | 200,000 |
| GPT-4o | 500 | 200,000 |
| GPT-5 Mini | 500 | 200,000 |
| GPT-4.1 Nano | 1,000 | 4,000,000 |
| o3 | 500 | 200,000 |
Key observations about OpenAI’s system:
- Budget models get higher limits. GPT-4.1 Nano at Tier 1 already has 4M TPM — 20x more than GPT-5 at the same tier. OpenAI clearly expects high-volume usage of their cheapest model and provisions accordingly.
- Tier 5 is extremely generous. At 30M TPM for GPT-5, you can process roughly 500K tokens per second. Very few applications need more than this.
- The free tier is almost unusable. 3 RPM and 200 RPD means you can make about 3 requests per minute, capped at 200 per day. This is for experimentation only.
- Rate limits are per-model. Using GPT-5 and o3 simultaneously gives you separate allocations for each. This is a meaningful advantage for applications that route across multiple models.
Anthropic (Claude) — Four-Tier System
Anthropic uses a spend-based tier system similar to OpenAI, but with four tiers instead of five. The limits are notably lower than OpenAI at equivalent spend levels.
Claude Sonnet 4.5 Rate Limits:
| Tier | Spend Required | RPM | TPM | RPD |
|---|---|---|---|---|
| Tier 1 | $5+ credit | 50 | 40,000 | — |
| Tier 2 | $40+ | 1,000 | 80,000 | — |
| Tier 3 | $200+ | 2,000 | 160,000 | — |
| Tier 4 | $400+ | 4,000 | 400,000 | — |
Claude Opus 4.5 Rate Limits:
| Tier | RPM | TPM |
|---|---|---|
| Tier 1 | 50 | 20,000 |
| Tier 2 | 1,000 | 40,000 |
| Tier 3 | 2,000 | 80,000 |
| Tier 4 | 4,000 | 200,000 |
Claude Haiku 4.5 Rate Limits:
| Tier | RPM | TPM |
|---|---|---|
| Tier 1 | 50 | 50,000 |
| Tier 2 | 1,000 | 100,000 |
| Tier 3 | 2,000 | 200,000 |
| Tier 4 | 4,000 | 400,000 |
Key observations about Anthropic’s system:
- Claude has no free tier. You must add at least $5 in credits to get any API access. This is a barrier for prototyping and evaluation compared to providers like Google and xAI.
- Tier 1 is very restrictive. 50 RPM and 40K TPM for Sonnet means you can realistically handle about 10-20 concurrent users before hitting limits. Production apps need Tier 2 at minimum.
- The gap with OpenAI is significant. At the $50+ spend level, OpenAI gives you 5,000 RPM and 2M TPM for GPT-5. Anthropic gives you 1,000 RPM and 80K TPM for Sonnet — 5x fewer requests and 25x fewer tokens per minute.
- Opus limits are even tighter. Opus 4.5 at Tier 2 gets only 40K TPM. If you are using the most expensive Claude model, you are also the most constrained on throughput.
Google Gemini — The Most Generous Free Tier
Google structures its rate limits differently from OpenAI and Anthropic. There is a free tier with genuinely useful limits, and a paid tier (pay-as-you-go) with high throughput.
Gemini 2.5 Pro Rate Limits:
| Plan | RPM | TPM | RPD |
|---|---|---|---|
| Free | 25 | 250,000 | 50 |
| Pay-as-you-go | 2,000 | 4,000,000 | — |
Gemini 2.5 Flash Rate Limits:
| Plan | RPM | TPM | RPD |
|---|---|---|---|
| Free | 500 | 1,000,000 | — |
| Pay-as-you-go | 4,000 | 4,000,000 | — |
Gemini 2.0 Flash (Legacy) Rate Limits:
| Plan | RPM | TPM | RPD |
|---|---|---|---|
| Free | 500 | 1,000,000 | — |
| Pay-as-you-go | 4,000 | 4,000,000 | — |
Key observations about Google’s system:
- The free tier is production-viable for small apps. Gemini 2.5 Flash at 500 RPM free is more than what Claude Sonnet gives you at Tier 1 (50 RPM) after paying $5. You can serve a small application with real users entirely for free.
- 4M TPM is the highest default limit of any provider. At the paid tier, both Pro and Flash deliver 4 million tokens per minute. OpenAI only reaches this level at Tier 3 ($100+ spend) for GPT-5, and Claude never reaches it on any standard tier.
- No tier system on paid plans. Once you switch to pay-as-you-go, you immediately get full rate limits. No need to spend $50, $100, or $1,000 to unlock higher tiers.
- Free tier Pro has a daily cap. 50 RPD for Gemini 2.5 Pro on the free tier is limiting, but the Flash free tier has no daily cap at all.
xAI (Grok) — Free Credits + Scaling Tiers
xAI provides $25 in free signup credits and structures rate limits that scale with usage.
Grok Rate Limits:
| Model | Free Tier RPM | Free Tier TPM | Paid RPM | Paid TPM |
|---|---|---|---|---|
| Grok 3 | 60 | 100,000 | Up to 1,200 | Up to 600,000 |
| Grok 3 Mini | 60 | 100,000 | Up to 1,200 | Up to 600,000 |
Key observations about xAI’s system:
- Free credits with reasonable limits. $25 of free credit at 60 RPM and 100K TPM is more useful than OpenAI’s free tier (3 RPM) and competitive with Anthropic’s Tier 1 (50 RPM). Enough to build and test a prototype.
- Paid limits scale gradually. Unlike Google’s flat structure, Grok’s limits increase as your usage grows, similar to OpenAI’s tier system but less formally defined.
- 600K TPM at higher tiers is solid — higher than Claude’s maximum standard tier (400K for Sonnet) but well below Gemini’s 4M.
DeepSeek — Flat Rate, No Tiers
DeepSeek takes the simplest approach of any major provider: flat rate limits with no tier system.
DeepSeek Rate Limits:
| Model | RPM | TPM |
|---|---|---|
| DeepSeek V3.2 (deepseek-chat) | 60 | 1,000,000 |
| DeepSeek R1 (deepseek-reasoner) | 60 | 1,000,000 |
Key observations about DeepSeek:
- Extremely generous TPM for the price. 1 million tokens per minute at $0.27/$1.10 per million tokens means you get massive throughput at rock-bottom pricing. No other provider offers this combination.
- RPM is the bottleneck. 60 RPM is low for high-concurrency applications. If you are making many short requests, you will hit the RPM limit long before TPM. This makes DeepSeek better suited for long-context, batch-style workloads than for chatbots serving many concurrent users.
- No tiers, no spend thresholds. You get the same limits from day one. No need to build up spend to unlock higher tiers. Simple and predictable.
Mistral — Free Tier Available
Mistral offers a free tier for experimentation and paid plans with competitive limits.
Mistral Rate Limits:
| Model | Free Tier RPM | Paid RPM |
|---|---|---|
| Mistral Large 3 | Lower (varies) | 300 |
| Mistral Medium 3 | Lower (varies) | 300 |
| Mistral Small 3.1 | Lower (varies) | 300 |
Key observations about Mistral:
- Free tier is available without a credit card, similar to Google. Useful for evaluation and prototyping.
- 300 RPM across all paid models is moderate — higher than DeepSeek (60) and Anthropic Tier 1 (50), but well below OpenAI Tier 2 (5,000) and Gemini (2,000-4,000).
- European data residency is Mistral’s unique advantage. Rate limits are not their differentiator — compliance is.
The Master Comparison Table
Here is every provider side by side at the paid tier that most startups and production applications use.
| Provider | Model | Tier/Plan | RPM | TPM | Min Spend |
|---|---|---|---|---|---|
| Gemini 2.5 Flash | Pay-as-you-go | 4,000 | 4,000,000 | $0 | |
| Gemini 2.5 Pro | Pay-as-you-go | 2,000 | 4,000,000 | $0 | |
| OpenAI | GPT-5 | Tier 3 | 5,000 | 4,000,000 | $100 |
| OpenAI | GPT-5 | Tier 5 | 10,000 | 30,000,000 | $1,000 |
| DeepSeek | V3.2 | Flat | 60 | 1,000,000 | $0 |
| xAI | Grok 3 | Paid (high) | 1,200 | 600,000 | — |
| Anthropic | Sonnet 4.5 | Tier 4 | 4,000 | 400,000 | $400 |
| Anthropic | Haiku 4.5 | Tier 4 | 4,000 | 400,000 | $400 |
| Mistral | Large 3 | Paid | 300 | — | $0 |
Ranking by TPM (tokens per minute):
- OpenAI Tier 5 — 30,000,000 TPM (requires $1,000+ spend)
- Google Gemini — 4,000,000 TPM (immediate, no spend threshold)
- OpenAI Tier 3 — 4,000,000 TPM (requires $100+ spend)
- DeepSeek — 1,000,000 TPM (flat, no tiers)
- xAI Grok — 600,000 TPM (higher paid tiers)
- Anthropic Tier 4 — 400,000 TPM (requires $400+ spend)
The pattern is clear: Google offers the highest immediately-available throughput, OpenAI catches up and eventually surpasses everyone at Tier 5, and Anthropic consistently trails on raw throughput at every spend level.
Comparison by Use Case
For Prototyping and Evaluation (Free Tier)
If you are just getting started, experimenting with models, or building a proof of concept, here is what each provider offers at zero cost:
| Provider | Free RPM | Free TPM | Credit Card Required? | Notes |
|---|---|---|---|---|
| Gemini 2.5 Flash | 500 | 1,000,000 | No | Best free tier overall |
| Gemini 2.5 Pro | 25 | 250,000 | No | 50 RPD cap |
| Grok 3 | 60 | 100,000 | No | $25 free credit |
| DeepSeek V3.2 | 60 | 1,000,000 | No | Full limits from start |
| Mistral Small 3.1 | Yes | Yes | No | Rate-limited free access |
| OpenAI GPT-5 | 3 | 40,000 | No | Very limited |
| Claude Sonnet 4.5 | — | — | Yes | No free tier |
Winner: Gemini 2.5 Flash at 500 RPM with no credit card. You can build and serve a small production application entirely for free. DeepSeek is the runner-up with its massive 1M TPM, though the 60 RPM cap limits concurrency.
Worst: Anthropic has no free tier at all, and OpenAI’s 3 RPM free tier is borderline unusable for anything beyond single manual tests.
For Startups (Medium Volume: 1K-10K Requests/Day)
At this scale, you are past prototyping and need reliable throughput for real users. The key question is which provider gives you enough headroom without requiring a large upfront spend.
| Provider | Model | RPM | TPM | Monthly Min Spend |
|---|---|---|---|---|
| Gemini | 2.5 Pro | 2,000 | 4,000,000 | Pay-as-you-go |
| Gemini | 2.5 Flash | 4,000 | 4,000,000 | Pay-as-you-go |
| OpenAI | GPT-5 (Tier 2) | 5,000 | 2,000,000 | $50+ cumulative |
| OpenAI | GPT-5 (Tier 3) | 5,000 | 4,000,000 | $100+ cumulative |
| DeepSeek | V3.2 | 60 | 1,000,000 | Pay-as-you-go |
| Anthropic | Sonnet 4.5 (Tier 2) | 1,000 | 80,000 | $40+ cumulative |
| xAI | Grok 3 | Up to 1,200 | Up to 600,000 | Pay-as-you-go |
Winner: Gemini for raw throughput (4M TPM immediately), or OpenAI Tier 2-3 for the highest RPM (5,000) if your workload consists of many short requests.
Worst: Anthropic Tier 2 at 80K TPM. If your startup processes documents averaging 10K tokens each, you can only handle about 8 documents per minute. That is not enough for most production use cases, and you need to spend $200+ just to reach Tier 3’s 160K TPM.
For Enterprise (High Volume: 50K+ Requests/Day)
At enterprise scale, all providers offer custom rate limits through sales engagements. But here is what you get on standard plans:
| Provider | Model | Best Standard RPM | Best Standard TPM |
|---|---|---|---|
| OpenAI | GPT-5 (Tier 5) | 10,000 | 30,000,000 |
| Gemini 2.5 Pro | 2,000 | 4,000,000 | |
| Gemini 2.5 Flash | 4,000 | 4,000,000 | |
| Anthropic | Sonnet 4.5 (Tier 4) | 4,000 | 400,000 |
| DeepSeek | V3.2 | 60 | 1,000,000 |
Winner: OpenAI Tier 5 at 30M TPM is in a class of its own. If you are at enterprise scale and need the absolute highest throughput on standard plans, OpenAI is the clear choice. Gemini’s 4M TPM is the next best, available immediately without tier requirements.
Note on custom limits: At $5,000+/month spend, every major provider will negotiate custom rate limits. Contact sales teams directly for Anthropic, OpenAI, Google, and xAI if standard limits are insufficient.
How Rate Limits Affect Your Architecture
Rate limits are not just an API annoyance — they should influence your entire system design. Here are the architectural patterns that matter.
1. Queue Management and Backpressure
When your application receives more requests than your API rate limit can handle, you need a queue. The simplest approach is a token bucket algorithm that tracks your remaining RPM and TPM budget and delays requests when limits are close.
The critical mistake is not accounting for TPM limits separately from RPM limits. A system that only tracks RPM will work fine for short messages but fail spectacularly when a user submits a 50K-token document that consumes half your TPM budget in a single request.
2. Multi-Provider Failover
The most robust architecture uses multiple providers as fallbacks. When your primary provider returns a 429 (rate limit exceeded), route to a secondary:
- Primary: OpenAI GPT-5 (best overall quality)
- Failover 1: Gemini 2.5 Pro (same price, higher TPM)
- Failover 2: DeepSeek V3.2 (much cheaper, 1M TPM)
This gives you effective throughput that is the sum of all providers’ limits, not just one. With OpenAI Tier 3 + Gemini + DeepSeek, you get 4M + 4M + 1M = 9M TPM combined.
3. Token Estimation Before Sending
Pre-counting tokens before sending a request lets you predict whether it will push you over your TPM limit. This avoids wasting an API call (and consuming RPM budget) on a request that will be rejected anyway.
Use our AI Token Counter to understand token counts for different models. For programmatic estimation, the tiktoken library (Python) or gpt-tokenizer (JavaScript) provides exact counts for OpenAI models, and approximate counts for others.
4. Separate Rate Limit Pools per Model
OpenAI and Anthropic both allocate rate limits per model, not per account. This means using GPT-5 and GPT-4.1 Nano simultaneously gives you two separate pools. Architect your system to spread load across models:
- Route simple tasks to budget models (GPT-4.1 Nano, Gemini Flash, Haiku)
- Route complex tasks to flagship models (GPT-5, Claude Sonnet, Gemini Pro)
Each model has its own RPM and TPM allocation, effectively multiplying your total throughput.
Tips to Maximize Throughput
1. Use Batch API for Non-Real-Time Workloads
Both OpenAI and Anthropic offer Batch APIs that process requests asynchronously (typically within 24 hours). Batch requests are exempt from standard rate limits and come with a 50% price discount. If any part of your workload — content generation, data extraction, evaluation, nightly processing — does not need real-time responses, move it to the Batch API immediately.
This is the single highest-impact optimization for throughput-constrained applications.
2. Implement Exponential Backoff with Jitter
When you hit a rate limit (HTTP 429), do not retry immediately. Use exponential backoff with random jitter to spread retry attempts:
import time
import random
from openai import OpenAI, RateLimitError
client = OpenAI()
def call_with_retry(messages, max_retries=5):
for attempt in range(max_retries):
try:
return client.chat.completions.create(
model="gpt-5",
messages=messages
)
except RateLimitError as e:
if attempt == max_retries - 1:
raise
# Exponential backoff: 1s, 2s, 4s, 8s, 16s
base_wait = 2 ** attempt
# Add jitter: random 0-50% extra
jitter = base_wait * random.uniform(0, 0.5)
wait = base_wait + jitter
print(f"Rate limited. Waiting {wait:.1f}s (attempt {attempt + 1})")
time.sleep(wait)
raise Exception("Max retries exceeded")The jitter is important because without it, all your retry attempts (and those of other clients) happen at exactly the same time, causing another burst of 429s. Jitter spreads the retries across the backoff window.
3. Pre-Count Tokens to Avoid Wasted Requests
Every rejected request (429 error) wastes your RPM budget. By estimating token counts before sending, you can hold requests in a local queue until you have enough TPM headroom:
import tiktoken
def estimate_tokens(messages, model="gpt-5"):
"""Estimate total tokens for a request."""
enc = tiktoken.encoding_for_model(model)
total = 0
for msg in messages:
total += len(enc.encode(msg["content"])) + 4 # message overhead
total += 2 # reply priming
return total
# Before sending, check if we have budget
estimated = estimate_tokens(messages)
if estimated > remaining_tpm_budget:
# Queue the request instead of sending immediately
request_queue.append(messages)
else:
remaining_tpm_budget -= estimated
response = client.chat.completions.create(model="gpt-5", messages=messages)4. Route Burst Traffic to High-Limit Providers
If your application experiences traffic spikes, route the excess to the provider with the highest available limits. In practice, this means:
- Normal traffic: Use your preferred provider (e.g., OpenAI or Claude)
- Burst traffic: Overflow to Gemini (4M TPM, no tier requirements) or DeepSeek (1M TPM, flat limits)
This pattern keeps your primary provider’s quality for most requests while preventing 429 errors during peaks.
5. Upgrade Tiers Strategically
For OpenAI and Anthropic, your tier is based on cumulative spend, not monthly spend. This means:
- If you know you will need Tier 3+ limits, front-load your spending by purchasing credits early.
- OpenAI: $100 cumulative spend unlocks Tier 3 (4M TPM for GPT-5). That is a one-time threshold, not monthly.
- Anthropic: $200 cumulative spend unlocks Tier 3 (160K TPM for Sonnet). Again, one-time.
Plan your tier progression based on your growth projections, and purchase credits slightly ahead of when you need the higher limits.
6. Use Streaming to Improve Perceived Throughput
Streaming responses does not change your actual rate limits, but it allows you to start displaying output to users before the full response is complete. This reduces perceived latency and makes rate-limit-induced delays less noticeable. All major providers support streaming via server-sent events (SSE).
Rate Limit Error Handling — Production Pattern
Here is a more complete production-ready pattern that handles rate limits across multiple providers with automatic failover:
import time
import random
from openai import OpenAI, RateLimitError
# Initialize clients for multiple providers
openai_client = OpenAI()
gemini_client = OpenAI(
api_key="your-gemini-key",
base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
)
deepseek_client = OpenAI(
api_key="your-deepseek-key",
base_url="https://api.deepseek.com"
)
PROVIDERS = [
{"client": openai_client, "model": "gpt-5", "name": "OpenAI"},
{"client": gemini_client, "model": "gemini-2.5-pro", "name": "Gemini"},
{"client": deepseek_client, "model": "deepseek-chat", "name": "DeepSeek"},
]
def call_with_failover(messages, max_retries=3):
"""Try each provider in order, with retries per provider."""
for provider in PROVIDERS:
for attempt in range(max_retries):
try:
response = provider["client"].chat.completions.create(
model=provider["model"],
messages=messages
)
return response, provider["name"]
except RateLimitError:
if attempt < max_retries - 1:
wait = (2 ** attempt) + random.uniform(0, 1)
time.sleep(wait)
else:
print(f"{provider['name']} exhausted. Trying next provider.")
break
raise Exception("All providers rate-limited. Consider queuing this request.")This pattern ensures your application stays responsive even when individual providers are throttling you. The key is that rate limits are per-provider, so being rate-limited on OpenAI says nothing about your remaining capacity on Gemini or DeepSeek.
Provider Recommendation by Daily Volume
| Daily Requests | Best Provider | Why |
|---|---|---|
| Under 1,000 | Any provider | All handle this volume comfortably |
| 1,000 - 5,000 | OpenAI (Tier 2) or Gemini | 5,000 RPM (OpenAI) or 4,000 RPM (Gemini) |
| 5,000 - 20,000 | Gemini 2.5 Flash | 4,000 RPM + 4M TPM with no tier requirements |
| 20,000 - 50,000 | Gemini + DeepSeek failover | Combined 5M+ TPM, lowest effective cost |
| 50,000+ | OpenAI Tier 5 or custom enterprise | 30M TPM (OpenAI) or negotiated limits |
For token-heavy workloads (long documents, large context):
| Daily Token Volume | Best Provider | Why |
|---|---|---|
| Under 10M tokens | Any provider | All handle this at paid tier |
| 10M - 100M tokens | Gemini or DeepSeek | 4M and 1M TPM respectively, no tiers needed |
| 100M - 500M tokens | OpenAI Tier 3+ | 4M TPM at Tier 3, scaling to 30M at Tier 5 |
| 500M+ tokens | OpenAI Tier 5 + Gemini | Combined 34M TPM, or contact sales for custom |
The Hidden Cost of Low Rate Limits
Rate limits have a real financial impact beyond just throttled requests. When your application hits a 429, several things happen:
- Wasted compute. Your server processed the user’s request, built the prompt, estimated tokens — all before discovering the API will not accept it.
- User-facing latency. The retry delay (even with exponential backoff) adds seconds or minutes to response times. Users notice.
- Queue depth explosion. If incoming requests exceed your API throughput, queues grow unboundedly. You need either a cap (reject requests) or a very large buffer.
- Over-provisioning costs. To avoid hitting limits, many teams over-provision by buying higher tiers than their average usage requires — paying for headroom they rarely use.
This is why rate limits should be factored into your total cost analysis alongside per-token pricing. A provider that costs 20% more per token but offers 10x the throughput may actually be cheaper when you account for infrastructure complexity, queue management, and user experience impact.
Bottom Line
Rate limits in February 2026 vary dramatically across providers, and the differences are larger than most developers realize:
- Google Gemini offers the highest immediately-available throughput at 4M TPM with no tier system and no minimum spend. Combined with a genuinely useful free tier (500 RPM for Flash), Gemini is the best choice for applications that need reliable, high throughput from day one.
- OpenAI has the highest theoretical ceiling at 30M TPM (Tier 5), but you need $1,000+ in cumulative spend to unlock it. For most startups, Tier 2-3 at 2-4M TPM is more realistic and still competitive.
- Anthropic (Claude) has the most restrictive rate limits of any major provider. Even at Tier 4 ($400+ spend), Sonnet 4.5 maxes out at 400K TPM — 10x lower than Gemini’s standard paid tier. If you choose Claude for its quality, budget for the throughput constraints.
- DeepSeek offers a unique value proposition: 1M TPM with flat pricing and no tiers, but only 60 RPM. Best for batch-style workloads with long contexts rather than high-concurrency chatbots.
- xAI (Grok) sits in the middle with reasonable limits and free credits for getting started, though it cannot match Gemini or OpenAI on raw throughput.
For most production applications, the optimal strategy is: start with Gemini for free-tier development, add OpenAI as your primary provider at Tier 2+, and keep DeepSeek or Gemini Flash as a high-TPM failover for burst traffic. Use the Batch API for everything that does not need real-time responses.
Rate limits will continue to increase as providers scale their infrastructure, but in February 2026, these are the numbers you need to plan around.
Related tools and guides:
- AI Token Counter — Pre-count tokens before sending API requests
- AI Model Pricing Calculator — Compare costs across 40+ models
- How to Cut AI API Costs by 80% — 8 optimization strategies including batch API and model routing
- AI API Pricing Comparison 2026 — Full pricing table for all 7 major providers
- OpenAI API Pricing Guide 2026 — GPT-5, GPT-4.1, o3 pricing and tier details
- Claude API Pricing Guide 2026 — Opus, Sonnet, Haiku pricing and prompt caching
- Google Gemini API Pricing Guide 2026 — Gemini 2.5 Pro/Flash, free tier, 1M context
- Grok API Pricing Guide 2026 — Grok 3 pricing, $25 free credits
- DeepSeek API Pricing Guide 2026 — The cheapest capable AI model
- Mistral API Pricing Guide 2026 — EU-compliant, open-weight options