The Four That Matter
Most LLM decisions in 2026 come down to four names: Anthropic's Claude, OpenAI's GPT, Google's Gemini, and DeepSeek. They occupy different points on the quality/price curve, and the right answer is usually "different ones for different jobs." Here's the honest breakdown. All four are on LinkModel under one key.
Price (per 1M tokens, flagship tier)
| Flagship | Input | Output | |
|---|---|---|---|
| Claude | Opus 4.8 | $5 | $25 |
| GPT | GPT-5.5 | $5 | $30 |
| Gemini | 3.1 Pro | ~$2 | ~$12 |
| DeepSeek | V4 Flash | $0.44 peak / $0.22 off-peak | $1.32 peak / $0.66 off-peak |
DeepSeek remains the lowest-priced row in this comparison, but its direct API uses peak and off-peak time bands. Gemini undercuts the two Western flagships; Claude and GPT anchor the premium tier. Recheck every rate and context tier before budgeting.
Where Each One Wins
- Claude — the best judgment: nuanced instruction-following, honesty, agentic coding (SWE-bench leader), and a reputation for catching its own errors. Premium price, 1M context. The pick when correctness and tone matter most.
- GPT — the broadest ecosystem: natively omnimodal, native computer use, mature tooling and structured output, and a Codex line for agents. Strong all-rounder; slightly pricier output.
- Gemini — the best value at quality, plus native image generation in the family and huge context. Flash tiers are exceptional for cost; Search grounding is a real edge. Watch the 200K context price cliff.
- DeepSeek — the low-cost route: competitive capability, 1M context, a separate cached-input tier, and open weights. Trade-offs include data-residency review for the official API and workload-dependent quality differences.
Quirks Worth Knowing
- Claude pricing holds $5/$25 across the Opus 4.x line; caching (~90%) and Batch (50%) cut it hard.
- GPT output runs pricier ($30 on 5.5); a Nano tier ($0.20 input) has no Claude equivalent.
- Gemini bills thinking tokens in output on the 2.5 family; the >200K cliff doubles input.
- DeepSeek model IDs and legacy aliases can change; copy the active identifier from the provider or gateway catalog at deployment time.
Which Should You Use?
| Job | Winner |
|---|---|
| Hardest reasoning / correctness | Claude |
| Broad ecosystem / computer use | GPT |
| Best value at quality / multimodal | Gemini |
| Cheapest high-volume | DeepSeek |
The mature answer is all four, routed: DeepSeek for bulk, Gemini Flash for the mid-tier, Claude/GPT for the hard 5%. See best coding LLM API, cheapest LLM API, and the per-vendor guides (Claude, GPT, Gemini, DeepSeek).
Because all four share one chat request shape on LinkModel, routing between them is a config change, not four integrations — the pattern in build an AI app with multiple models.
Start free with a $1 credit and test all four on your own prompts.
A Fair Four-Model Comparison
Do not compare a premium reasoning model from one vendor with a budget model from another and call the result a provider verdict. Pick one current API model per workload and record its exact ID and date. Use the same prompt, tools, temperature, output cap, and pass criteria.
| Workload | Primary metric | Secondary metric |
|---|---|---|
| Extraction | field-level accuracy | cost per document |
| Coding | tests passed | repair time and tokens |
| Long-context QA | answer accuracy with citation | latency at context depth |
| Agent workflow | completed tasks | steps, tool errors, and retries |
| Customer response | rubric score | p95 latency and cost |
Published benchmark scores are useful for discovery, but your application can reverse the ranking through tool schemas, prompt length, and failure handling.
Route Instead of Picking One Winner
A production router can send structured bulk work to a low-cost model, ordinary application logic to a balanced model, and only uncertain or high-impact tasks to a premium model. Add a fallback for provider errors, but cap total attempts so failover does not become a cost multiplier.
The decision variable is cost per accepted task:
(token spend + retry spend + human repair cost) ÷ successful tasks
Re-run the suite after a model revision. Provider families evolve too quickly for a permanent winner.