The Cheapest OpenAI-Compatible LLM Gateways for Coding Agents (2026)

If you are choosing an API for a coding agent, “cheapest” is not a single number. The model, output length, tool loop, cache behavior, and the amount of integration work all change the bill. A useful comparison starts with a narrower question: which OpenAI-compatible LLM gateway lets this agent run the models I need, with prices and failure modes I can inspect?

This guide is a buying framework for independent developers running coding agents, not a ranking. It explains the dimensions that matter, shows a small set of options to investigate, and includes a reproducible request you can run before migrating a project. The examples use USD per million tokens, because that is the unit exposed by GlideflowAI’s public model menu. Treat every price as a dated configuration value, then verify it on the provider’s current pricing page before committing production traffic.

What an LLM gateway does

An LLM gateway is an API layer between an application and one or more model providers. For a coding agent, the useful version of that idea is usually simple: keep the client’s OpenAI-style request shape, point its base URL at one endpoint, and select a model ID per job.

That is different from an automatic model router. A router chooses a model for you according to a policy. A gateway can expose several models while leaving the choice in the agent configuration or in your own code. For a solo developer, explicit selection is often easier to reason about: use one model for planning, another for mechanical edits, and measure the resulting token use separately.

OpenAI compatibility is valuable because much of the surrounding tooling already understands baseURL, apiKey, chat-completions-style messages, and streaming. It is not a promise that every model will behave identically. Tool-call format, reasoning-token use, context limits, latency, and instruction-following are model-specific. Keep those distinctions in your test plan.

Why coding agents make price comparison harder

A chat prompt may have one input and one answer. A coding agent tends to loop: inspect files, propose a plan, call tools, read outputs, edit files, test, and recover from errors. That loop has three consequences.

First, output tokens can dominate. A model that emits detailed plans, diffs, or tool arguments may consume far more output than a short Q&A session. Comparing only input-token price can produce the wrong conclusion.

Second, context is a cost and reliability variable. Re-sending a large repository summary, test logs, or generated documentation on every turn is expensive regardless of the per-token headline. Look for an agent that truncates stale history, summarize deliberately, and use caching where the chosen model and API support it.

Third, a model change has an engineering cost. If a new endpoint requires a custom SDK, a request-shape rewrite, or a different credential flow, a low token price can be offset by integration time. A gateway with a compatible API shape can reduce that switching cost, but the right test is still a real repository task.

Five checks before calling a gateway “affordable”

1. Request compatibility

Start with the interface your agent already uses. Can it take an OpenAI-compatible base URL? Does it need chat completions, responses, an Anthropic-compatible endpoint, streaming, or tool calls? A plain curl request is only a first check. Run the same minimal task from the actual agent harness, including at least one tool call if your workflow uses tools.

Ask the provider for exact model IDs rather than guessing from a marketing name. A model ID mismatch is a configuration problem, not evidence about model quality or gateway reliability.

2. Transparent input and output prices

Record both prices in the unit your provider uses. GlideflowAI lists USD prices per one million tokens in its model catalog. For example, the current configured prices below are input/output pairs; they are not estimates of a completed coding task.

Those figures are menu data, not a claim about another provider’s price or a recommendation for every task. The important habit is to capture the pair and calculate against your own token logs. For example, a run that uses 2M input tokens and 0.5M output tokens on GLM-5.2 is priced as 2 × 0.90 + 0.5 × 2.95 before any feature-specific charges. Use the same arithmetic for every candidate.

3. A model menu you can understand

More models are not automatically better. A massive catalog can be useful for experimentation, but it also creates selection work and inconsistent defaults. For coding agents, begin with a shortlist that covers the jobs you actually have: a long-context coding route, an agentic coding route, a lower-cost route for repeated mechanical work, and perhaps a stronger route for difficult planning.

GlideflowAI’s stated approach is a curated menu across Claude, Gemini, GPT, Qwen, GLM, Kimi, and DeepSeek families, exposed through a unified API. That may suit a developer who values a smaller, inspectable choice set. A broad catalog service may suit a team that needs a provider or specialized model outside that menu. Neither preference is a benchmark result; it is an operational trade-off.

4. Pay-as-you-go mechanics and billing visibility

For agent work, it is useful to see the price before choosing a model and to attribute calls after the fact. Check whether prices are public, whether the dashboard reports usage by model, and whether the minimum top-up or subscription arrangement fits your development cycle. A transparent price table does not eliminate spend risk: an unbounded agent loop can consume a budget quickly at any rate.

Set a project budget and a per-run ceiling in the agent if the tool supports it. Store the selected model ID alongside the run’s token counts. This makes a later comparison meaningful: you can distinguish a cheaper model route from a shorter prompt, a smaller repository, or a test that simply stopped earlier.

5. Evidence from your own workflow

The last check is a repeatable task, not a vibes-based ranking. Pick a small repository issue with tests. Run each candidate with the same prompt, branch, tool permissions, and max-turn policy. Record whether it completed the task, the number of turns, input/output tokens, human cleanup time, and any failures. Repeat with a second task that has a different shape, such as a bug fix after a test failure.

This produces a decision table you can maintain:

Do not turn one successful run into a promise about every repository. Coding tasks vary too much for that.

A fair option overview

There are several reasonable starting points, and their documentation should be part of the evaluation. OpenRouter is commonly considered when a developer wants a broad, multi-provider model catalog. Provider-direct APIs are a natural fit when a project is standardized on one family and wants that provider’s native features. Gateway products such as GlideflowAI focus on putting a selected menu behind a unified API shape. Other infrastructure layers can help with observability, retries, or policy controls.

The practical question is not which label wins. It is whether the product supports your required protocol and models, publishes enough pricing information for a calculation, and lets you carry out a controlled test. Keep provider and gateway documentation open while evaluating. Do not infer a provider’s availability, capability, or rate policy from a model name alone.

Where GlideflowAI fits

GlideflowAI is an OpenAI-compatible AI API gateway designed around a curated menu and transparent, pay-as-you-go USD or crypto pricing. For an OpenAI-style client, the API base URL is:

https://api.glideflowai.com/v1

The point of the setup is not to claim that all routes behave the same. It is to make a controlled model comparison easier: preserve the client integration, change the model ID, and keep token accounting beside the results. Browse the model catalog for the current menu and exact IDs.

Run a compatibility check before migrating

Create a restricted test key and use a harmless prompt first. Do not put a real key in a client-side application or a public repository.

export GLIDEFLOW_API_KEY="sk-your-key"

curl https://api.glideflowai.com/v1/chat/completions \
  -H "Authorization: Bearer $GLIDEFLOW_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model":"glm-5.2",
    "messages":[{"role":"user","content":"Reply with OK."}],
    "max_tokens":512
  }'

The 512 output-token allowance is deliberate. Reasoning-oriented models may spend part of a small token budget on internal work and return no visible text with a very low limit. Treat a response as a connectivity check, not a benchmark.

Here is the matching Python client setup:

from openai import OpenAI

client = OpenAI(
    api_key="sk-your-key",
    base_url="https://api.glideflowai.com/v1",
)

reply = client.chat.completions.create(
    model="glm-5.2",
    messages=[{"role": "user", "content": "Reply with OK."}],
    max_tokens=512,
)
print(reply.choices[0].message.content)

And Node.js:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.GLIDEFLOW_API_KEY,
  baseURL: "https://api.glideflowai.com/v1",
});

const reply = await client.chat.completions.create({
  model: "glm-5.2",
  messages: [{ role: "user", content: "Reply with OK." }],
  max_tokens: 512,
});

console.log(reply.choices[0].message.content);

Once this works, use one narrow repository task and record the response, tokens, and test result. Then repeat with kimi-k2.7-code or a Qwen route using the exact IDs from the catalog. That experiment is more useful than a generic “best gateway” list.

Cost controls belong in the agent, too

Gateway selection cannot compensate for an agent that has no stopping rule. Put simple controls around the experiment before it runs against a meaningful repository. Set a maximum turn count, limit the directories it may edit, and require a test command before the agent declares success. When the task needs a long command output, ask the agent to summarize the relevant lines instead of repeatedly pasting the full log into later turns.

Use a separate key or project boundary for experiments where possible. This makes it easier to inspect usage without mixing an evaluation run with day-to-day development. It also makes a failed configuration less disruptive: revoke the test key, correct the endpoint or model ID, then start a clean run. These practices are ordinary engineering controls, but they make token-price comparisons far more useful.

A small selection workflow

Use this sequence to keep the decision reversible:

1. List the protocols your agent actually needs: OpenAI-compatible chat, streaming, tool calls, or an Anthropic-compatible path.
2. Select two or three model IDs with distinct roles; do not test twenty at once.
3. Run the same issue and tests with a fixed turn budget.
4. Calculate token cost from separate input and output prices.
5. Review quality, retries, and cleanup time together with the total.
6. Keep a fallback route for the class of task that did not work well.

That workflow works whether you settle on a direct provider, a broad catalog, or a curated gateway. It also prevents a common failure mode: optimizing the cheapest individual token rate while overlooking a model that needs twice as many turns.

What to read next

For the catalog and the current model menu, visit /models. The related setup guide explains how to point Claude Code at a configured model. For a model-level discussion, see GLM-5.2, Kimi K2.7, and Qwen3.7 for coding agents.