AI & Prompt Tools · Free tool
AI Feature Comparison Matrix
Vision, audio, video, tool use, web search, code interpreter, file upload, voice mode, memory, agents — across ChatGPT, Claude, Gemini, Perplexity, and 6 more.
Updated June 2026
| Tool | Pricing | Image input | Audio input | Video gen | Tool use | Web search | Code interpreter | File upload | Voice mode | Long-term memory | Agentic mode |
|---|---|---|---|---|---|---|---|---|---|---|---|
| ChatGPT (Plus/Pro) | $20-200/mo | ✓ | ✓ | Sora | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Claude (Pro/Max) | $20-100/mo | ✓ | − | − | ✓ | ✓ | ✓ | ✓ | − | ✓ | ✓ |
| Gemini (Advanced) | $20-250/mo | ✓ | ✓ | Veo | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Perplexity (Pro) | $20/mo | ✓ | − | − | ✓ | ✓ | − | ✓ | ✓ | − | ✓ |
| DeepSeek | Free + API | ✓ | − | − | ✓ | ✓ | ✓ | ✓ | − | − | − |
| Kimi (Moonshot) | Free + API | ✓ | − | − | ✓ | ✓ | − | ✓ | − | ✓ | − |
| Grok (X Premium) | $8-40/mo | ✓ | − | − | ✓ | ✓ | − | − | ✓ | ✓ | − |
| Mistral (Le Chat) | Free + API | ✓ | − | − | ✓ | ✓ | ✓ | ✓ | − | − | − |
| NotebookLM | Free | ✓ | Audio overviews | Video overviews | − | − | − | ✓ | ✓ | − | − |
| Microsoft Copilot | Free + $30 | ✓ | ✓ | − | ✓ | ✓ | − | ✓ | ✓ | ✓ | ✓ |
Feature parity is moving fast — this matrix tracks 2026 Q1 state. The headline differences in 2026: Gemini owns native multimodal (audio + video both ways); Claude owns long-running agents; ChatGPT owns ecosystem breadth (custom GPTs, Sora, voice, search); Perplexity owns research / sourced answers; DeepSeek + Kimi own price-to-quality.
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What it does
Vision, audio, video, tool use, web search, code interpreter, file upload, voice mode, memory, agents — across ChatGPT, Claude, Gemini, Perplexity, and 6 more. Selecting the right AI tool for a given task is the single biggest cost lever in modern AI workflows.
AI-product reliability depends on rate limits, latency, and provider uptime — not just model quality. The gap between “rough estimate” and “defensible number” is exactly where good tooling earns its keep — the math is reproducible, but knowing which inputs matter and what the result means is half the work.
Batch APIs (50% discount on async work) dominate cost-per-token for analysis pipelines that don’t need real-time response. A common pitfall: ignoring rate limits until production launch. Treat the tool’s output as a starting point and validate against authoritative sources for any consequential decision.
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- Open the tool and review the interface.
- Enter or paste your input.
- Configure any relevant options.
- Run the tool and review the output.
- Iterate or refine based on the result.
When to use this tool
- Pre-launch budget planning for an LLM-powered feature.
- Comparing API costs vs self-hosting for high-volume workloads.
- Production cost forecasting based on traffic projections.
- Prompt-engineering optimization to reduce token consumption.
When not to use it
- When you have negotiated enterprise pricing not reflected in public rate cards.
- For hyper-bursty traffic where peak load determines architecture, not average.
- When the workload is unique enough that public benchmarks don’t apply.
- For non-frontier image, video, or audio model pricing (those use per-asset billing).
Common use cases
- A indie creators experimenting with AI tools working through ai feature comparison matrix for a real decision.
- A ML engineers optimizing inference costs working through ai feature comparison matrix for a real decision.
- A developers building LLM features working through ai feature comparison matrix for a real decision.
- A researchers comparing model quality working through ai feature comparison matrix for a real decision.
Frequently asked questions
- How does self-hosting change the math?
- Self-hosting Llama 3.3 70B on AWS p4d ($32/hr) costs ~$16/M tokens at full utilization. DeepSeek V3 API is $0.30/M tokens. Self-hosting wins only at 1B+ tokens/month consistent.
- Should I switch to a smaller model?
- Probably yes, after testing. Mini / Haiku tier handles 60-70% of production tasks adequately at 5-10x lower cost. Test on your specific workload, then route only failures to the larger model.
- What about prompt caching and batch discounts?
- Prompt caching saves 50-90% on cached input tokens (OpenAI: 50%; Anthropic: up to 90% with 5-minute cache). Batch API: 50% off async jobs. Combined, can drop bills 70-80% for cache-friendly workloads.
- Is this calculation accurate at scale?
- Public-rate-card calculators are accurate within 10-15% for typical workloads. Variance comes from prompt-cache hit rates, batch-API usage, and rate-limit retry overhead.
- How does this compare to GPT-4o or Claude Opus 4?
- GPT-4o, Claude Opus 4, and Gemini 2.5 Pro are roughly comparable on quality for general tasks; their pricing differs by 30-50% so test on your specific workload before locking in.
- What hidden costs am I missing?
- Output tokens (3-5x input cost), rate-limit retry overhead (20-40% extra), failed-request charges, and the engineering time to maintain the integration. Budget 1.5-2x the headline rate.
See how this compares
- Head-to-headClaude vs ChatGPTClaude vs ChatGPT compared head-to-head: coding, writing, reasoning, agents, voice, vision, pricing, and which one to pick for your real workflow in 2026.
- Head-to-headClaude vs GeminiClaude vs Gemini head-to-head: coding, long context (1M vs 2M), multimodal, agents, pricing, and which to pick for your real 2026 workflow.
- Head-to-headClaude vs PerplexityClaude vs Perplexity compared: research, citations, coding, agents, search quality, pricing — and why most heavy users pay for both.
- Head-to-headChatGPT vs GeminiChatGPT vs Gemini in 2026: coding, multimodal, integrations, voice mode, video generation, pricing, and which one fits your workflow.
- Head-to-headChatGPT vs PerplexityChatGPT vs Perplexity compared: research, citations, voice, agents, pricing — and why these tools complement each other instead of replacing one another.
- Head-to-headGemini vs PerplexityGemini vs Perplexity head-to-head: research depth, citations, multimodal, video generation, pricing, and which fits your workflow in 2026.
- Head-to-headClaude vs GrokClaude vs Grok 4 compared: coding, agents, real-time data via X, voice mode, pricing, and which AI to pick for your real workflow.
- Head-to-headKimi K2 vs GeminiKimi K2 vs Gemini 2.5/3 Pro compared: context window (1M vs 2M), multimodal, open weights, pricing, and which long-context AI to use.
- Head-to-headChatGPT Plus vs Claude ProChatGPT Plus vs Claude Pro head-to-head: features, usage caps, multimodal, agents, and which to pick for your daily AI work.
- Head-to-headPerplexity Pro vs ChatGPT PlusPerplexity Pro vs ChatGPT Plus compared: research, voice, agents, model picker, and which $20/mo AI subscription to pick.
- Head-to-headPerplexity vs Google SearchPerplexity vs Google Search head-to-head: answer quality, citations, AI Overviews, speed, and when each wins for research.
- Head-to-headClaude Projects vs Custom GPTsClaude Projects vs ChatGPT Custom GPTs compared: persistent context, file uploads, sharing, agents, and which fits your workflow.
- Head-to-headGemini Advanced vs ChatGPT PlusGemini Advanced vs ChatGPT Plus compared: video gen, voice, agents, Workspace integration, and which $20/mo plan fits.
- Head-to-headSora 2 vs Veo 3Sora 2 vs Veo 3 head-to-head: cinematography, controllability, physics, max length, audio, pricing. Pick by output style + cost.
- Head-to-headMidjourney vs FLUXMidjourney v7 vs FLUX 1.1 Pro Ultra: photorealism, style, controllability, pricing. Pick by output need + workflow.
- Head-to-headClaude Team vs ChatGPT TeamClaude Team vs ChatGPT Team for orgs: features, admin controls, pricing, data handling, and which to standardize on.
- Head-to-headPerplexity Pro vs You.com ProPerplexity Pro vs You.com Pro for AI research: model picker, citations, Spaces, Genius Mode. Pick by research workflow.
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Learn more
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