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

digitado ⋅ 26 de January de 2026

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

Context: can you fit the job in one request?
Cost: can you afford volume?
Latency: does your UX tolerate the wait?
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.

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.

| Model | Input / 1M | Cached input / 1M | Output / 1M | When to use |
|—-|—-|—-|—-|—-|
| gpt-4.1 | $2.00 | $0.50 | $8.00 | High-quality general reasoning with sane cost |
| gpt-4o | $2.50 | $1.25 | $10.00 | Multimodal-ish “workhorse” if you need it |
| gpt-4o-mini | $0.15 | $0.075 | $0.60 | High-throughput chat, extraction, tagging |
| o3 | $2.00 | $0.50 | $8.00 | Reasoning-heavy tasks without the top-end pricing |
| o1 | $15.00 | $7.50 | $60.00 | “Use sparingly”: hard reasoning where mistakes are expensive |

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.

| Model | Input / MTok | Output / MTok | Notes |
|—-|—-|—-|—-|
| Claude Haiku 4.5 | $1.00 | $5.00 | Fast, budget-friendly tier |
| Claude Haiku 3.5 | $0.80 | $4.00 | Even cheaper tier option |
| Claude Sonnet 3.7 (deprecated) | $3.75 | $15.00 | Listed as deprecated on pricing |
| Claude Opus 3 (deprecated) | $18.75 | $75.00 | Premium, but marked deprecated |

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.

| Tier (example rows from pricing page) | Input / 1M (text/image/video) | Output / 1M | Notable extras |
|—-|—-|—-|—-|
| Gemini tier (row example) | $0.30 | $2.50 | Context caching + grounding options |
| Gemini Flash-style row example | $0.10 | $0.40 | Very low output cost; good for high volume |

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:

TTFT (time to first token) — how fast streaming starts
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)

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:

Fast cheap tier for most requests
Strong tier for hard prompts, long context, tricky reasoning
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.

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