DEEP DIVE · 09
Claude vs ChatGPT
vs Gemini — full comparison
MIXI already uses the ChatGPT API in production. This page is framed around adding Claude to that stack, not replacing it.
CONTENTS · 11 SECTIONS
01Framing the comparison
02Model performance
03Coding ability
04Long-context ability
05Multimodal
06Agent capabilities
07Enterprise features
08Japan-market fit
09Lock-in & migration
10Workload-by-workload
11"Add" design principles
A full comparison of the three major LLM vendors (Anthropic / OpenAI / Google) as of 2026-04-23, framed from MIXI's existing ChatGPT investment. Where public data isn't settled, entries are marked ※ verify.
1. Framing the comparison
MIXI already runs the ChatGPT API in production, integrated across multiple workflows. Given that starting point, the question isn't "which one is strongest." It's "where does adding Claude pay off versus the existing ChatGPT footprint?"
- Existing investment: ChatGPT API usage patterns, prompt assets, SDK integrations, and SSO / audit plumbing are already in place. There's no good reason to zero-reset any of that.
- Decision frame: "One vendor exclusive (replace)" is obsolete. "Multi-vendor by workload (add)" is the 2026 enterprise default.
- Three quadrants: (1) areas where Claude is meaningfully stronger than ChatGPT, (2) areas where they're roughly equal and switching costs don't pay back, (3) areas where ChatGPT or Gemini is stronger.
- Target models: Claude Opus 4.7 / Sonnet 4.6 / Haiku 4.5 · ChatGPT GPT-5.4 / GPT-5 mini · Gemini 3 Pro / 3 Flash / 3.1 Flash Live. GPT-4o and Gemini 2.5 Pro are treated as "previous generation" here and excluded from new selection.
MIXI recommendation: Inventory the ChatGPT API estate first. Classify each workload into "worth switching to Claude," "worth running both," and "keep on ChatGPT as-is" — then enter rollout design.
2. Model performance table
Flagship models side-by-side. Prices are per 1M tokens, public list. Because vendor pricing pages shift, specific dollar figures are marked ※ see official and only ranges / relative position are fixed here.
| Model | Vendor | Generation | Context | Input / 1M | Output / 1M | Benchmark position |
|---|---|---|---|---|---|---|
| Claude Opus 4.7 | Anthropic | 2026-04 | 1M (beta) | $15 | $75 | SWE-bench frontier, long-context reasoning |
| Claude Sonnet 4.6 | Anthropic | 2026-02 | 1M (beta) | $3 | $15 | The production workhorse, best balance |
| Claude Haiku 4.5 | Anthropic | 2025-10 | 200K | $1 | $5 | Low-latency, high-volume |
| GPT-5.4 (flagship) | OpenAI | 2026 | ※ see official | ※ | ※ | Competes with Opus on reasoning and tool use |
| GPT-5 mini | OpenAI | 2026 | ※ | ※ | ※ | Cost-optimized GPT-5, high-throughput workloads |
| Gemini 3 Pro | 2026 | ~2M ※ | ※ | ※ | Ultra-long context, video understanding, multilingual | |
| Gemini 3.1 Flash Live | 2026 | ※ | ※ | ※ | Realtime bidirectional audio + video streaming |
Data label. Current GPT-5.x and Gemini 3.x prices and context lengths require a fresh check against the vendor pricing pages — this table only locks in relative position. For internal citations, re-verify ※-flagged numbers against openai.com/api/pricing and ai.google.dev/pricing. Previous-generation models (GPT-4o, Gemini 2.5 Pro, Gemini 1.5 Flash) are intentionally out of scope here.
3. Coding ability
Since Q1 2026, coding has been Claude's decisive advantage. The reason isn't just raw model capability — it's the maturity of the Claude Code environment around it.
| Dimension | Claude Code (Anthropic) | GitHub Copilot (GPT-5.4) | Cursor / Windsurf (Gemini 3 etc.) |
|---|---|---|---|
| SWE-bench Verified | Frontier (Opus 4.7) | Top tier | Top tier |
| CLI / Desktop / IDE / Web surfaces | All four (Desktop GA) | IDE / Web focus | IDE focus |
| Long autonomous runs | Stable across hours–days | Short sessions | Medium sessions |
| MCP tool extension | Native, official registry | Limited | Limited |
| Context ceiling | 1M beta, high effective accuracy | ※ see official | ~2M headline, medium effective |
| Agent SDK | Agent SDK (OSS) | — | — |
MIXI recommendation: Lean the code-gen / refactor / long batch coding work toward Claude Code — that's where ROI is strongest. Keep GitHub Copilot for day-to-day in-editor completion; run Claude Code for "long, large, autonomous" work. A two-layer operating model.
4. Long-context ability
Headline context window and effective accuracy are not the same number. Past 100K tokens, degradation curves diverge sharply by model.
| Dimension | Claude (Opus 4.7 / Sonnet 4.6) | ChatGPT (GPT-5.4) | Gemini 3 Pro |
|---|---|---|---|
| Stated context | 1M (beta) / 200K std | ※ see official | ~2M ※ |
| Effective accuracy (100K+) | High (strong on needle-in-haystack) | Medium–High | Medium (tail degrades) |
| Long-context cost efficiency | Prompt Cache — up to 90% off | Batch API (50% off) | Context Caching supported |
| Typical use cases | Contract review, large-code audit, bundled meeting minutes | General RAG, email summarization | Video + subtitles, multilingual docs |
MIXI recommendation: Workloads that need hundreds of pages in one shot — contract review, quarterly minute bundles, large-scale code audit — favor Claude. Any existing ChatGPT flow that splits documents into chunks can usually be replaced by a single Claude call and drop the chunking machinery.
5. Vision & multimodal
Images, video, and audio split cleanly by vendor. No one model dominates every modality.
| Modality | Claude | ChatGPT | Gemini |
|---|---|---|---|
| Image understanding (text, charts) | Strong (UI / charts / screenshots) | Strong (general) | Strong |
| Image generation | External (no native generation) | Native (gpt-image / GPT-5.x) | Native (Imagen family) |
| Video understanding | Limited ※ verify | Frame-extract based | Native video input (strong) |
| Audio input | Mobile voice mode | Realtime API (GPT-5.x) | Gemini 3.1 Flash Live |
| Audio output (TTS / realtime) | Limited | Strong (Realtime API) | Strong (Gemini 3.1 Flash Live) |
MIXI recommendation: Keep ChatGPT / Gemini for game-NPC voice conversation, realtime audio UX, and image generation. Video analysis (stream summarization, UGC moderation) is strongest on Gemini right now. Static document-plus-image analysis is already well-served by Claude.
6. Agent capabilities
2026 is the year the shift from chat to agents went mainstream. Each vendor is pushing its own surface.
| Capability | Anthropic | OpenAI | |
|---|---|---|---|
| Primary surfaces | Claude Code, Agent SDK, Microsoft Copilot Cowork (GA 2026-02-24) | ChatGPT Agent, Operator, Assistants API | Vertex Agent Builder, Gemini 3.1 Flash Live |
| Tool integration standard | MCP (native; 800+ servers in the official registry) | MCP-compatible (added 2025), function calling | MCP-compatible, Extensions |
| Long autonomous runs | Ahead on hours-to-days stability | Expanding via ChatGPT Agent | Expanding via Agent Builder |
| Computer Use | Beta — Claude drives the PC directly | Operator (web-focused) | Limited |
| SDK maturity | Agent SDK OSS, shared runtime with Claude Code | Assistants API, Agents SDK | Vertex AI Agent Builder |
MIXI recommendation: Long-running business agents (quarterly report generation, automated code review, automated contract review) are cheapest to implement on the Claude Agent SDK. Short, interactive agents are well-served by ChatGPT Agent and can ride on the existing investment.
7. Enterprise features
Cross-check on SSO / ZDR / audit logs / Japan region. All three vendors check the must-have boxes in their enterprise plans — the differentiator is the procurement route and contract shape.
| Feature | Anthropic Claude | OpenAI ChatGPT | Google Gemini |
|---|---|---|---|
| SSO / SAML / SCIM | Yes (Team / Enterprise) | Yes (Enterprise) | Yes (Workspace-integrated) |
| ZDR (Zero Data Retention) | Yes (Enterprise addendum) | Yes (Enterprise / API) | Yes (via Vertex) |
| Audit logs | Yes (Enterprise, SIEM-connected) | Yes (Enterprise Compliance API) | Yes (Cloud Audit Logs) |
| SOC 2 Type II | Yes | Yes | Yes |
| ISO 27001 / 27701 / 42001 | Yes | Yes ※ verify 42001 | Yes |
| HIPAA BAA | Yes | Yes | Yes |
| Japan residency | AWS Bedrock Tokyo, Vertex Tokyo | Azure OpenAI East Japan | Vertex AI Tokyo |
| Custom retention | Yes (0 days to any duration) | Yes (Enterprise) | Yes (Vertex) |
MIXI recommendation: All three vendors cover enterprise requirements, so enterprise features alone won't make the selection decision. The real question to resolve first is which cloud (Bedrock / Azure / Vertex) is the sanctioned IT procurement path — that decision automatically constrains the candidate LLMs.
8. Japan-market fit
Comparison across four axes: language quality, data residency, Japanese support, local case studies. Japanese-language performance has reached practical parity across all three vendors in 2026 — "can it speak Japanese" is no longer a differentiator.
| Dimension | Claude | ChatGPT | Gemini |
|---|---|---|---|
| Japanese generation quality | High (strong on keigo / business writing) | High (general) | High (multilingual native) |
| Japan region (residency) | AWS Bedrock Tokyo (Opus 4.7 available from 2026-04-20) | Azure OpenAI East Japan | Vertex AI Tokyo |
| Japanese support channel | AWS / Anthropic Japan team | Microsoft / OpenAI Japan | Google Cloud Japan |
| Domestic case studies | Growing across finance / telecom / manufacturing ※ verify | Largest accumulated base | Retail and media case studies |
| Billing / contract currency | USD / JPY (via cloud) | USD / JPY (via Azure) | USD / JPY (via GCP) |
MIXI recommendation: Japanese output quality is "sufficient" at all three. The decisive factors are the residency routing and the accounting clean-fit for billing. Start by deciding which of MIXI's existing AWS / Azure / GCP contracts the procurement will ride on.
9. Lock-in & migration cost
This is the most important lens for the "replace vs add" call. What exactly would need to be rewritten to migrate an existing ChatGPT API investment to Claude?
| Layer | Migration effort | Notes |
|---|---|---|
| Prompt body | Low | 80%+ of natural-language prompts carry over unchanged. Minor styling adjustments. |
| API endpoint / SDK | Medium | OpenAI SDK → Anthropic SDK or Bedrock SDK. A thin wrapper absorbs the difference. |
| Function calling / tool definitions | Medium | JSON Schema is nearly identical. Return format has small differences. |
| Structured output | Medium | OpenAI uses JSON mode / Strict; Claude uses tool-use or prefill in combination. |
| Embeddings / vector DB | High (incompatible) | Claude has no native embeddings — keep using Voyage / Cohere / OpenAI embeddings as the realistic path. |
| Fine-tuned models | Cannot migrate | Vendor-specific. Any move requires a re-train. |
| Prompt cache design | Medium | Claude offers 90% off; OpenAI also supports prompt caching. Design philosophies are close. |
| Skills / MCP assets | Low | agentskills.io / MCP are multi-vendor standards. Reusable across vendors. |
MIXI recommendation: Prompts, Skills, and MCP are reusable across vendors — so running "two vendors in parallel" costs less than expected. Embeddings and fine-tuned models are where lock-in actually bites. Keep the fine-tuned ChatGPT workloads in place; bring Claude in from new workloads onward.
10. Workload-by-workload recommendations
Fifteen workloads mapped to "this vendor for this job" — written with MIXI's business (games / SNS / payments / sports / daily-life infrastructure) in mind.
| # | Workload | Recommended vendor / model | Why |
|---|---|---|---|
| 01 | Contract review (hundreds of pages) | Claude Opus 4.7 | Long-context accuracy, legal-document quality |
| 02 | Executive decks / board reports | Claude Sonnet 4.6 + Design skill | Structured documents, figure instructions |
| 03 | Code generation (long autonomous) | Claude Code (Opus 4.7) | SWE-bench frontier, autonomous stability |
| 04 | Code completion (in-editor, daily) | GitHub Copilot (existing) | Existing investment, low latency |
| 05 | Game NPC voice conversation | ChatGPT Realtime (GPT-5.x) / Gemini 3.1 Flash Live | Bidirectional low latency, TTS quality |
| 06 | Image generation (marketing) | GPT-image (GPT-5.x) / Imagen | Native image generation |
| 07 | Video analysis (UGC moderation) | Gemini 3 Pro | Native video input |
| 08 | Multilingual translation (20+ languages) | Gemini 3 Pro | Multilingual coverage |
| 09 | Customer support responses | ChatGPT (existing) + Haiku 4.5 backup | Existing investment, low-cost fallback |
| 10 | SQL generation / BI helper | Claude Sonnet 4.6 + BigQuery MCP | Long schema handling, MCP integration |
| 11 | Internal knowledge search (RAG) | Claude Sonnet 4.6 + existing embeddings | Long-context fidelity, answer quality |
| 12 | Meeting minutes / cleanup | Claude Haiku 4.5 (volume) / Sonnet 4.6 (important meetings) | Cost, Japanese keigo |
| 13 | Engineer-side technical research | Claude Code + web search | Long research runs, structured output |
| 14 | Security audit / code vulnerability | Claude Opus 4.7 | Deep reasoning, long-code handling |
| 15 | Realtime voice UX (game) | ChatGPT Realtime / Gemini 3.1 Flash Live | Bidirectional low latency |
How to read this: Across 15 workloads — Claude leads on 8, ChatGPT / Copilot on 4, Gemini on 3. No reason to remove ChatGPT. Adding Claude expands overall capability coverage.
11. "Add" design principles for MIXI
Seven concrete design principles for adding Claude on top of the existing ChatGPT footprint.
- Write down the division of roles. "Claude = Code / long-context / agents", "ChatGPT = general / voice / image generation", "Gemini = video / multilingual translation" — captured as a one-page internal guideline.
- Put a router layer in between. Don't let apps call vendor SDKs directly. Route through an internal LLM router that selects a vendor per workload. That way, swapping or adding vendors becomes a few-line change downstream.
- Make MCP / Skills a shared asset. MCP connectors to Slack / GitHub / BigQuery / Datadog, and internal Skills, should be treated as cross-vendor assets usable across Claude / ChatGPT / Gemini. The only layer where you can invest without lock-in.
- Don't force-migrate existing fine-tunes. Keep the fine-tuned ChatGPT workloads as-is while performance and cost pencil out. Try Claude first on new workloads.
- Choose embeddings independently. Lock Voyage / Cohere / OpenAI embeddings at the vector-DB layer so the generation layer can span vendors.
- Cost cap + model-downgrade path. Any workload sent to Opus 4.7 must have a "Sonnet 4.6 fallback" or "Haiku 4.5 fallback". Keep peak-time cost anchored on Sonnet as the mainstay.
- Normalize procurement paths. Anthropic (Bedrock Tokyo / Vertex Tokyo), OpenAI (Azure OpenAI), Google (Vertex AI) — all go through sanctioned IT procurement. No individually distributed API keys.
Conclusion: For MIXI, Claude adoption isn't "an investment to replace ChatGPT." It's "an investment to add an option that outperforms on Code / long-context / agents." A formation that preserves existing investment while widening coverage. For rollout specifics, see deep-security, deep-api-keys, and deep-cowork.
Official sources
Last verified: 2026-04-23
- Claude pricing: https://claude.com/pricing
- OpenAI pricing: https://openai.com/api/pricing
- Google AI for Developers (Gemini pricing): https://ai.google.dev/pricing
- Claude docs: https://docs.claude.com
- Anthropic Trust Center: https://trust.anthropic.com
- AWS Bedrock Claude: https://aws.amazon.com/bedrock/claude/
- Google Vertex AI Claude: cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude
- Microsoft Azure AI Foundry: azure.microsoft.com/products/ai-foundry
- MCP spec: https://modelcontextprotocol.io
- Skills standard: https://agentskills.io