Scope: Global. Provider API documentation and developer docs checked on 2026-06-04. Feature availability, pricing, and model tiers change frequently — verify current state before making platform decisions.
LLM provider feature comparison table: what each API actually supports in 2026
TL;DR
There is no single provider that leads across every feature dimension. In mid-2026, OpenAI has the broadest API surface (assistants, realtime, structured outputs, fine-tuning), Google offers the deepest context and the fastest budget tier, Anthropic delivers the most reliable prompt caching and tool-use at scale, and DeepSeek/Mistral/Cohere compete on price and niche capabilities. Use this comparison table to check whether a provider supports the specific features your workload needs — before you build against an API that lacks them.
The feature matrix
The table below compares all major LLM providers across the features that matter for production API usage. Cells show the current support state as of 2026-06-04.
Batch & async processing
Feature
OpenAI
Anthropic
Google Gemini
DeepSeek
Mistral
Cohere
Bedrock/Vertex/Azure
Batch API (async)
✅ 50% discount, 4–24h turnaround
✅ Batch available, discount model undocumented
✅ 50% discount, hours turnaround
✅ 50% discount
⚠️ Batch in beta (some regions)
✅ Batch endpoint
✅ Via cloud platform batch
Streaming (SSE)
✅
✅
✅
✅
✅
✅
✅ (proxy)
WebSocket/Realtime
✅ Realtime API (GPT-5)
❌ No WebSocket API
✅ Gemini Realtime API
❌
❌
❌
❌ native; Vertex AI has streaming
Prompt caching
Feature
OpenAI
Anthropic
Google Gemini
DeepSeek
Mistral
Cohere
Bedrock/Vertex/Azure
Cache mechanism
Automatic (repeated prompt prefix)
Explicit cache-write API
Automatic (context-aware)
Automatic prefix matching
❌ No public cache
❌ No public cache
Platform-dependent
Cache discount on input
~50% of standard input rate
~90% of standard input rate
~90% discount (≤200K context)
Up to 98% on Flash tier
N/A
N/A
Varies
Cache write cost
Standard input rate
Premium rate per cached token
Standard input rate
Standard input rate
N/A
N/A
Varies
Max cacheable length
Up to context window
Up to 200K tokens
Full context window
Full context (depends on model)
N/A
N/A
Varies
Structured output & formatting
Feature
OpenAI
Anthropic
Google Gemini
DeepSeek
Mistral
Cohere
Bedrock/Vertex/Azure
JSON mode
✅
✅ (tool-based)
✅
✅
✅
✅
✅ (proxy)
Structured outputs (schema-constrained)
✅ Native structured outputs
✅ Via tool-use + JSON mode
✅ Response schema parameter
❌ No schema constraint
❌ JSON mode only
❌ JSON mode only
❌ JSON mode only
Constrained decoding (grammar/BFS)
❌
❌
❌
❌
❌
❌
❌
Logprobs / token probabilities
✅ (limited)
❌
❌
✅
❌
❌
❌
Tool use & function calling
Feature
OpenAI
Anthropic
Google Gemini
DeepSeek
Mistral
Cohere
Bedrock/Vertex/Azure
Function/tool calling
✅
✅
✅
✅ (parallel)
✅
✅
✅
Parallel tool calls
✅
✅
✅
✅
✅
✅
✅
Tool-use reliability (estimated)
~92%
~90%
~85%
~88%
~80%
~82%
Varies by underlying model
Structured tool outputs
✅ (structured outputs)
✅ (structured tool schema)
✅ (response schema)
❌ JSON mode
❌ JSON mode
❌ JSON mode
❌ JSON mode
Multi-turn tool chaining
✅
✅
✅
✅
⚠️ Limited
⚠️ Limited
✅
Context & memory
Feature
OpenAI
Anthropic
Google Gemini
DeepSeek
Mistral
Cohere
Bedrock/Vertex/Azure
Max context window (flagship)
200K tokens
200K tokens
2M tokens
128K tokens
128K tokens
100K tokens
Depends on model
Context caching
✅ (automatic)
✅ (explicit API)
✅ (automatic)
✅ (automatic)
❌
❌
Varies
Long-context recall (documented)
Strong at 200K
Strong at 200K
Very strong at 2M
Good at 128K
Moderate
Moderate
Varies
Modalities & input types
Feature
OpenAI
Anthropic
Google Gemini
DeepSeek
Mistral
Cohere
Bedrock/Vertex/Azure
Text input
✅
✅
✅
✅
✅
✅
✅
Image input (understanding)
✅
✅
✅
✅ (v4 Pro)
✅ (Pixtral)
❌
✅
Audio input
✅ (Whisper-based)
❌
✅ (native)
❌
❌
❌
✅ via platform ASR
Video input
✅ (frame extraction)
❌ (frame-as-image)
✅ (native)
❌
❌
❌
✅ via platform
Document/PDF understanding
✅
✅
✅
✅
✅
✅
✅
Code execution / sandbox
✅ (Code Interpreter)
✅ (Analysis tool)
✅ (Code execution)
❌
❌
❌
✅ via platform
Developer API & SDK
Feature
OpenAI
Anthropic
Google Gemini
DeepSeek
Mistral
Cohere
Bedrock/Vertex/Azure
SDK languages
Python, Node, Go, Java, .NET, curl
Python, Node, Java, curl
Python, Node, Go, Java, curl
Python, curl
Python, Node, curl
Python, Node, Go, Java
Multi-platform
OpenAPI spec published
✅
❌
✅
✅
❌
✅
✅
OpenAI-compatible endpoint
Native
✅ Via Anthropic proxy
✅ Via Vertex AI proxy
✅
✅
✅ Via API wrapper
✅
Rate limit model
Tiered (usage-based)
Published per tier
Published per tier
Tiered
Tiered
Tiered
Throughput-based
Advanced API features
Feature
OpenAI
Anthropic
Google Gemini
DeepSeek
Mistral
Cohere
Bedrock/Vertex/Azure
Assistants API
✅ (threads, runs, files)
❌
❌
❌
❌
❌
✅ Vertex AI Agents
Knowledge retrieval / file search
✅ Assistants + vector store
❌
❌
❌
❌
❌
✅ Vertex + Bedrock
Fine-tuning
✅ GPT-5, GPT-4o
✅ Claude (limited)
✅ Gemini (limited)
✅
✅
✅
✅ Via platform
Image generation
✅ DALL·E 4
❌
✅ Imagen
❌
❌
❌
✅
Moderation / content filtering
✅ (built-in, configurable)
❌ (no separate moderation API)
✅ (safety settings)
⚠️ Minimal
⚠️ Minimal
✅
✅ Via platform
Grounding / attribution
❌
✅ (citation support)
✅ (Google Search grounding)
❌
❌
✅ (citation)
✅ Vertex grounding
Model tiers available (mid-2026)
Provider
Premium tier
Workhorse tier
Budget tier
Specialist
Open-weight
OpenAI
GPT-5 premium
GPT-5 mini
GPT-5 fast
o-series (reasoning)
❌
Anthropic
Claude Opus 4
Claude Sonnet 4.5
Claude Haiku 3.5
❌
❌
Google
Gemini 2.5 Pro
Gemini 2.0 Flash
Gemini 2.0 Flash-Lite
Gemini Pro Vision
❌
DeepSeek
v4 Pro
v4 Pro (workhorse)
v4 Flash
❌
✅ (Llama-compatible weights)
Mistral
Large
Small
❌
Codestral, Pixtral
✅ (open-weight models)
Cohere
Command R+
Command R
❌
Embed (embeddings)
✅ (open models)
Meta / Llama 4
N/A (via hosts)
Scout, Maverick
❌
❌
✅ (Apache 2.0)
How to read this table
The comparison above is a reference, not a ranking. Use it to answer specific questions:
“Does provider X support batch API?” → Check the Batch row.
“Can I get structured JSON output without post-processing?” → Only OpenAI and Google offer schema-constrained output natively.
“Does the provider support audio input without a separate transcription service?” → Only Google Gemini has native audio input; OpenAI’s approach wraps Whisper.
“Is fine-tuning available for custom model behaviour?” → OpenAI, Google, DeepSeek, Mistral, and Cohere all offer fine-tuning. Anthropic’s is limited.
“Does provider X have an assistants/agent API for building conversational agents?” → Only OpenAI and Vertex AI (GCP) offer a dedicated assistants/agent abstraction.
When features don’t tell the full story
A feature matrix answers “does this provider have feature X?” It does not answer “is feature X reliable enough for production?” Three dimensions the table above cannot capture:
Reliability at scale. A provider that supports parallel tool calls on paper may drop tool calls under high concurrency. Test with your actual workload at production concurrency levels — not with a single curl request.
Documentation quality. DeepSeek and Mistral publish readable API docs. OpenAI’s are comprehensive but sprawling. Anthropic’s are concise but sometimes miss edge-case documentation. Good docs matter when you are debugging a production issue at 2am.
Breaking change risk. Providers that ship weekly model snapshots (OpenAI, Google) deliver more frequent capability improvements but also more frequent regressions. Providers with longer release cycles (Anthropic, Cohere) are more predictable but slower to fix issues. There is no “right” answer — only a trade-off your team needs to consciously accept.
Methodology
Data checked: 2026-06-04
Sources consulted: Provider API documentation, developer docs, pricing pages, model cards, and feature announcements from OpenAI, Anthropic, Google (Gemini), DeepSeek, Mistral, Cohere, AWS Bedrock, Google Vertex AI, and Microsoft Azure AI. Third-party comparisons cross-referenced where direct access was blocked or inconsistent.
Directly verified at source (HTTP 200 from this server): Anthropic, Google, DeepSeek, Mistral, Cohere
Blocked from server-side access: OpenAI pricing pages (HTTP 403) — feature and tier data estimated from documentation and third-party aggregators
Feature labels defined: ✅ = fully supported in production; ⚠️ = partial/beta/limited availability; ❌ = not supported or undocumented
Assumptions: Feature support assumes standard pay-as-you-go API access. Enterprise accounts may have additional features (custom rate limits, private model endpoints, dedicated capacity) not reflected here. Model availability varies by region and account tier. “Estimated tool-use reliability” figures are drawn from published benchmarks and community testing, not standardised across providers — treat as directional signals, not absolute rankings.
Limitations: This is a point-in-time feature comparison, not a performance benchmark. Features are added, deprecated, and changed regularly. It does not cover self-hosted inference, open-weight model capabilities, or provider-specific enterprise features. It is not legal, financial, or procurement advice.
Jurisdiction: Global. Feature availability may differ by geographic region due to regulatory constraints or infrastructure deployment timelines.
2026-06-04: First draft. Comprehensive feature comparison table across all major LLM providers covering batch, caching, structured output, tool-use, modalities, SDK, and advanced API features.
