Choosing an LLM in 2026: The Practical Comparison Table (Specs, Cost, Latency, Compatibility)

The uncomfortable truth: “model choice” is half your prompt engineering

If your prompt is a recipe, the model is your kitchen.

A great recipe doesn’t help if:

• the oven is tiny (context window),
• the ingredients are expensive (token price),
• the chef is slow (latency),
• or your tools don’t fit (function calling / JSON / SDK / ecosystem).

So here’s a practical comparison you can actually use.

Note on “parameters”: for many frontier models, parameter counts are not publicly disclosed. In practice, context window + pricing + tool features predict “fit” better than guessing parameter scale.

1) Quick comparison: what you should care about first

1.1 The “four knobs” that matter

1. Context: can you fit the job in one request?
2. Cost: can you afford volume?
3. Latency: does your UX tolerate the wait?
4. Compatibility: will your stack integrate cleanly?

Everything else is second order.

2) Model spec table (context + positioning)

This table focuses on what’s stable: family, positioning, and context expectations.

| Provider | Model family (examples) | Typical positioning | Notes | |---|---|---| | OpenAI | GPT family (e.g., gpt-4o, gpt-4.1, gpt-5*) | General-purpose, strong tooling ecosystem | Pricing + cached input are clearly published. | OpenAI | “o” reasoning family (e.g., o3, o1) | Deep reasoning / harder planning | Often higher cost; use selectively. | Anthropic | Claude family (e.g., Haiku / Sonnet tiers) | Strong writing + safety posture; clean docs | Pricing table includes multiple rate dimensions. | Google | Gemini family (Flash / Pro tiers) | Multimodal + Google ecosystem + caching/grounding options | Pricing page explicitly covers caching + grounding. | DeepSeek | DeepSeek chat + reasoning models | Aggressive price/perf, popular for scale | Official pricing docs available. | Open source | Llama / Qwen / Mistral etc. | Self-host for privacy/control | Context depends on model; Llama 3.1 supports 128K.

3) Pricing table (the part your CFO actually reads)

Below are public list prices from official docs (USD per 1M tokens). Use this as a baseline, then apply: caching, batch discounts, and your real output length.

3.1 OpenAI (selected highlights)

OpenAI publishes input, cached input, and output prices per 1M tokens.

If you’re building a product: you’ll often run 80–95% of calls on a cheaper model (mini/fast tier), and escalate only the hard cases.

3.2 Anthropic (Claude)

Anthropic publishes a model pricing table in Claude docs.

Important: model availability changes. Treat the pricing table as the authoritative “what exists right now.”

3.3 Google Gemini (Developer API)

Gemini pricing varies by tier and includes context caching + grounding pricing.

Gemini’s pricing page also lists:

• context caching prices, and
• grounding with Google Search pricing/limits.

3.4 DeepSeek (API)

DeepSeek publishes pricing in its API docs and on its pricing page.

4) Latency: don’t use fake “average seconds” tables

Most blog latency tables are either:

• measured on one day, one region, one payload, then recycled forever, or
• pure fiction.

Instead, use two metrics you can actually observe:

1. TTFT (time to first token) — how fast streaming starts
2. Tokens/sec — how fast output arrives once it starts

4.1 Practical latency expectations (directional)

• “Mini/Flash” tiers usually win TTFT and throughput for chat-style workloads.
• “Reasoning” tiers typically have slower TTFT and may output more tokens (more thinking), so perceived latency increases.
• Long context inputs increase latency everywhere.

4.2 How to benchmark for your own product (a 15-minute method)

Create a small benchmark script that sends:

• the same prompt (e.g., 400–800 tokens),
• fixed max output (e.g., 300 tokens),
• in your target region,
• for 30–50 runs.

Record:

• p50 / p95 TTFT,
• p50 / p95 total time,
• tokens/sec.

Then make the decision with data, not vibes.

5) Compatibility: why “tooling fit” beats raw model quality

A model that’s 5% “smarter” but breaks your stack is a net loss.

5.1 Prompt + API surface compatibility (what breaks when you switch models)

| Feature | OpenAI | Claude | Gemini | Open-source (self-host) | |---|---|---|---| | Strong “system instruction” control | Yes (explicit system role) | Yes (instructions patterns supported) | Yes | Depends on serving stack | | Tool / function calling | Widely used in ecosystem | Supported via tools patterns (provider-specific) | Supports tools + grounding options | Often “prompt it to emit JSON”, no native tools | | Structured output reliability | Strong with constraints | Strong, especially on long text | Strong with explicit schemas | Varies a lot; needs examples + validators | | Caching / batch primitives | Cached input pricing published | Provider features vary | Context caching explicitly priced | You implement caching yourself |

5.2 Ecosystem fit (a.k.a. “what do you already use?”)

• If you live in Google Workspace / Vertex-style workflows, Gemini integration + grounding options can be a natural fit.
• If you rely on a broad third-party automation ecosystem, OpenAI + Claude both have mature SDK + tooling coverage (LangChain etc.).
• If you need data residency / on-prem, open-source models (Llama/Qwen) let you keep data inside your boundary, but you pay in MLOps.

6) The decision checklist: pick models like an engineer

Step 1 — classify the task

• High volume / low stakes: tagging, rewrite, FAQ, extraction
• Medium stakes: customer support replies, internal reporting
• High stakes: legal, finance, security, medical-like domains (be careful)

Step 2 — decide your stack (the “2–3 model rule”)

A common setup:

1. Fast cheap tier for most requests
2. Strong tier for hard prompts, long context, tricky reasoning
3. Optional: realtime or deep reasoning tier for specific UX/features

Step 3 — cost control strategy (before you ship)

• enforce output length limits
• cache repeated system/context
• batch homogeneous jobs
• add escalation rules (don’t send everything to your most expensive model)

7) A practical comparison table you can paste into a PRD

Here’s a short “copy/paste” table for stakeholders.

Final take

“Best model” is not a thing.

There’s only best model for this prompt, this latency budget, this cost envelope, and this ecosystem.

If you ship with:

• a measured benchmark,
• a 2–3 model stack,
• strict output constraints,
• and caching/batching,

…you’ll outperform teams who chase the newest model every month.