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. ## Source list - [OpenAI API documentation — models](https://platform.openai.com/docs/models) — accessed 2026-06-04 - [OpenAI API reference](https://platform.openai.com/docs/api-reference) — accessed 2026-06-04 - [OpenAI structured outputs guide](https://platform.openai.com/docs/guides/structured-outputs) — accessed 2026-06-04 - [Anthropic Claude API reference](https://docs.anthropic.com/en/api/getting-started) — accessed 2026-06-04 - [Anthropic model comparison](https://docs.anthropic.com/en/docs/about-claude/models) — accessed 2026-06-04 - [Anthropic prompt caching](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching) — accessed 2026-06-04 - [Google Gemini API models and features](https://ai.google.dev/gemini-api/docs/models) — accessed 2026-06-04 - [Google Gemini API — structured output](https://ai.google.dev/gemini-api/docs/structured-outputs) — accessed 2026-06-04 - [Google Gemini API — caching](https://ai.google.dev/gemini-api/docs/caching) — accessed 2026-06-04 - [DeepSeek API documentation](https://api-docs.deepseek.com/) — accessed 2026-06-04 - [DeepSeek API pricing](https://api-docs.deepseek.com/quick_start/pricing) — accessed 2026-06-04 - [Mistral AI documentation](https://docs.mistral.ai/) — accessed 2026-06-04 - [Cohere API reference](https://docs.cohere.com/reference) — accessed 2026-06-04 - [AWS Bedrock documentation](https://docs.aws.amazon.com/bedrock/) — accessed 2026-06-04 - [Google Vertex AI documentation](https://cloud.google.com/vertex-ai/docs) — accessed 2026-06-04 - [Microsoft Azure AI documentation](https://learn.microsoft.com/en-us/azure/ai-services/) — accessed 2026-06-04 - [Artificial Analysis model comparison](https://artificialanalysis.ai/models) — accessed 2026-06-04 (cross-reference for OpenAI feature availability) ## Related guides - [LLM Model Scorecard: GPT-5, Claude 4, and Gemini 3 compared side-by-side](/comparisons/llm-model-scorecard-frontier-comparison/) — model quality, pricing, and benchmark comparison - [How to choose an LLM provider: decision framework for teams](/comparisons/how-to-choose-an-llm-provider-decision-framework/) — the structured methodology for shortlisting providers - [LLM API pricing comparison 2026](/cache/llm-api-pricing-comparison-2026/) — detailed pricing with worked cost examples - [Cloud AI platforms vs direct model APIs](/comparisons/cloud-ai-platforms-vs-direct-model-apis/) — platform-layer decision once you pick a provider - [Function calling and tool use: where agents actually fail](/cache/function-calling-and-tool-use-where-agents-actually-fail/) — deeper dive on tool-use reliability across providers - [Prompt caching explained](/cache/prompt-caching-explained-when-repeated-context-becomes-cheaper/) — understanding cache economics across providers - [Multimodal models explained](/cache/multimodal-models-explained-text-images-audio-and-video-in-practical-products/) — understanding modality differences across providers ## Trust Stack - **Last checked:** 2026-06-04 - **Corrections:** [Contact us](/contact/) to report errors ## Change log - **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.