Modelflare Review: AI API Gateway for GPT, Claude, Grok & More

Modelflare is a hosted AI API gateway: one API key and one OpenAI-compatible endpoint that routes to GPT, Claude, Grok, Gemini, DeepSeek, and other model families, with price-tiered routing groups, ordered fallback, and per-request cost tracking. In this review we break down what it actually does, how to wire it into existing apps and coding agents like Codex and Claude Code, what it really costs, where it beats OpenRouter/LiteLLM/Portkey, and where it falls short.

Quick Verdict

Modelflare is a hosted multi-provider gateway best suited to developers who are already juggling several AI vendor accounts and want one key, one bill, and one log stream instead of five. Its strongest points are the OpenAI-compatible API (plus native Anthropic and Gemini protocols), transparent price-tiered routing groups that list per-token prices against official rates, ordered fallback between model groups, and first-class setup docs for Codex, Claude Code, Gemini CLI, and OpenCode.

The honest limits: the catalog is small (25 models at the time of writing) compared with OpenRouter; the operator, Havenbyte LLC, is a young company with no SOC 2 or ISO certifications claimed; the cheapest routing tier is explicitly positioned for retryable workloads rather than production traffic; subscription credits only apply to GPT, Claude, and Gemini and do not roll over; and there is no self-hosted option. If you need a large model marketplace, compliance certifications, or full infrastructure control, look elsewhere. If you want a simple, low-friction gateway with visible per-request costs, Modelflare is worth a look.

What Is Modelflare?

Modelflare, operated by Havenbyte LLC, sits between your application and the AI providers. Instead of managing an OpenAI key, an Anthropic key, an xAI key, and a Google key separately, your app talks to Modelflare using one key, and Modelflare forwards each request to the selected model or model group. It speaks the protocols your tools already use: OpenAI Chat Completions and the Responses API at https://modelflare.dev/v1, and the native Anthropic Messages and Gemini protocols at https://modelflare.dev.

That last mile matters for developer tools. Because Modelflare implements the OpenAI, Anthropic, and Gemini wire protocols, tools that let you override a base URL — Codex, Claude Code, Gemini CLI, OpenCode, CC-Switch, Grok Build — can be pointed at Modelflare without code changes. We cover those setups below.

What Problem Does Modelflare Solve?

If you build with more than one model provider, the pain is familiar:

• Key sprawl. Separate API keys, billing dashboards, and rate limits for OpenAI, Anthropic, xAI, and Google. Modelflare collapses them into one key and one balance.
• Vendor lock-in in the small. Hard-coding provider SDKs makes switching models a refactor. A gateway turns "switch model" into "change a model string."
• Provider outages. When a single provider has a bad hour, your product has a bad hour. Ordered fallback between model groups lets requests reroute automatically.
• Cost blindness. Official consoles show spend per vendor, not per feature, per API key, or per agent. Modelflare logs model, tokens, status, latency, and cost for every request in one place.
• Agent wiring overhead. Every CLI agent has its own config format. Modelflare ships per-tool setup guides and installers so each tool gets its own key against a shared balance.

These are the same problems the broader "AI gateway" category addresses — see our reviews of the tools that sit on top of this layer, like Claude Code vs Cursor, for where gateways fit in a modern AI dev stack.

Key Modelflare Features

Based on Modelflare's official site, docs, and trust pages as of August 28, 2026:

• Multi-model access. 25 enabled models across OpenAI, Anthropic, xAI, Google, DeepSeek, Moonshot (Kimi), and Alibaba (Qwen) — plus image generation (GPT Image 2) and video (Seedance 2.5) endpoints.
• OpenAI-compatible API. Chat Completions and Responses API at https://modelflare.dev/v1; native Anthropic Messages and Gemini protocols at https://modelflare.dev.
• Model groups. Named routing groups per model at different price/stability tiers (e.g. openai-award, openai-stable, openai-premium), selected per API key or per tool config.
• Ordered fallback. Priority-ordered fallback groups in the dashboard; requests switch automatically when the primary group is unavailable.
• Per-key access control. Create separate API keys per app, tool, or agent, each with its own group assignment.
• Request logs. Status, latency, token usage, and cost per request, in real time.
• Cache Hit Guarantee. Eligible requests get daily cache hit-rate protection, with shortfalls compensated — verifiable in the usage dashboard.
• Broad regional availability. Commercial API access across 150+ listed countries and regions (effective August 1, 2026).
• Documented data handling. Request/response content is not stored as a standard long-term record; diagnostics expire after 72 hours; the optional Request Archive is off by default with a 168-hour expiry.

OpenAI-Compatible API: Three Values and You're Done

The core integration contract is deliberately boring, which is a compliment. Any client that already speaks the OpenAI API only needs three values changed:

That's the entire migration story for OpenAI-SDK-based apps. Because Anthropic Messages and Gemini native protocols are also supported at the root base URL, tools that speak those protocols natively (like Claude Code and Gemini CLI) work without an OpenAI shim. Model IDs are Modelflare-specific — you pick them from the live catalog, and per-model support differs: some models are available via Chat Completions only, others also via the Responses API, Anthropic Messages, or Gemini native calls.

How to Connect an Existing Application

The docs describe a three-step path: create an API key, set the base URL, send the first request. In practice, for an app already using the OpenAI SDK:

1. Create an API key in the Modelflare dashboard — one key per app, tool, or agent, each mapped to a routing group.
2. Set the base URL to https://modelflare.dev/v1 and the key in your client's auth header.
3. Set the model to a catalog ID such as gpt-5.6-sol (optionally pin a routing group like openai-stable where the tool supports it).
4. Send a request and verify the response, then check usage — tokens, status, latency, and cost appear in the dashboard.

A minimal Chat Completions request looks like this:

cURL

curl https://modelflare.dev/v1/chat/completions \
  -H "Authorization: Bearer YOUR_MODELFLARE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.6-sol",
    "messages": [{ "role": "user", "content": "Hello" }]
  }'

Note: exact model IDs, supported protocols, and routing-group availability vary per model and change over time — always cross-check the live catalog and docs before hard-coding values. We verified the examples above against Modelflare's published quick-start documentation on August 28, 2026.

Using Modelflare With Developer Tools

This is where Modelflare's integration work pays off. It publishes dedicated quick starts for Codex, Claude Code, Gemini CLI, OpenCode, CC-Switch, and Grok Build. The three most search-relevant setups:

Modelflare with Codex

Codex talks to OpenAI-compatible providers, so Modelflare provides a one-line installer (curl -fsSL https://modelflare.dev/install/codex.sh | sh, with PowerShell and root/container variants) that writes a Modelflare provider into ~/.codex/config.toml. Manual setup is equally short:

~/.codex/config.toml

# ~/.codex/config.toml
model_provider = "modelflare"
model = "gpt-5.6-sol"
model_routing_group = "openai-stable"

Launch with codex --model gpt-5.6-sol. Two things worth knowing: the routing group is set in the config (e.g. openai-stable), and the docs' troubleshooting table flags that a model_not_found error usually means the API key's group can't access that model — a reminder that key, group, and model must line up. For background on the tool itself, see our Best AI Coding Assistant 2026 guide.

Modelflare with Claude Code

Claude Code speaks the Anthropic Messages protocol natively, so the integration is an environment override rather than a provider plugin. Modelflare again offers an installer, or the manual route — set the base URL and key in Claude Code's settings:

settings.json

// Claude Code settings.json
{
  "env": {
    "ANTHROPIC_BASE_URL": "https://modelflare.dev",
    "ANTHROPIC_API_KEY": "YOUR_MODELFLARE_API_KEY"
  }
}

At the time of writing, the documented supported models are claude-opus-4-8 and claude-sonnet-5 — a narrower Claude selection than the full catalog, so check the quick start before assuming a specific Claude model works. Context on the tool: our Claude Code vs Cursor and Claude Code vs GitHub Copilot comparisons cover where it fits.

Modelflare with Gemini CLI and OpenCode

Gemini CLI uses two environment variables: GEMINI_API_KEY (your Modelflare key) and GOOGLE_GEMINI_BASE_URL=https://modelflare.dev, since it speaks the Gemini native protocol. OpenCode uses the OpenAI-compatible https://modelflare.dev/v1 endpoint with models like gpt-5.6-sol. Modelflare also documents CC-Switch (a provider switcher for Claude Code-style tools) and Grok Build setups, though we'd only add those if they match your stack.

Model Groups & Ordered Fallback

Two mechanisms do the heavy lifting in Modelflare's routing story.

Model groups are named routes per model at different price and stability tiers. For gpt-5.6-sol, the catalog lists openai-award (price-priority, for retryable workloads), openai-stable (the go-to for everyday development), and openai-premium (billed higher, prioritizing continuity and request success). You select a group per API key or per tool config — which means, for example, your CI test suite can use the cheap retryable route while your production key uses the stable one.

Ordered fallback is configured in the dashboard: you add fallback groups in priority order, and when the primary group is unavailable, requests switch automatically to the next one. Be precise about what this is: it is availability-based rerouting between groups, not a semantic quality router. Modelflare's own copy says requests switch "when the primary group is unavailable" — the mechanism does not evaluate which model would answer best, and a fallback group may route to a different model or provider entirely, which can change output characteristics mid-outage.

Usage, Tokens & Cost Tracking

Every request that passes through Modelflare is logged with its model, routing, status, latency, token counts, cache usage, and cost. Because each API key is a separate entity — one per app, tool, or agent — the dashboard effectively becomes a per-key cost breakdown without any instrumentation code on your side.

That is the quiet killer feature for anyone running AI features in production. "What did the onboarding assistant cost last week?" or "which agent burned the tokens?" becomes a dashboard filter instead of a logging pipeline you have to build and maintain. The trust documentation also states what is not retained: request/response content is not stored as a standard long-term record, short-term diagnostics expire after 72 hours, and the optional Request Archive is off by default with a 168-hour expiry.

Pricing

Checked against Modelflare's live pricing page on August 28, 2026. The catalog showed 25 enabled models. Prices change — treat this section as a snapshot, not a quote.

Modelflare prices per model and per routing group, in multipliers of the official API list price. The clearest documented example is gpt-5.6-sol (official list: $5 input / $30 output per million tokens):

Other models follow the same group structure (Claude and several GPT models listed with 3-4 routing groups; Grok, Gemini, DeepSeek, Kimi, and Qwen models with one group each at the time of checking). Beyond pay-per-token, Modelflare sells prepaid subscription plans that add bonus credits:

Read the fine print on subscriptions: credits apply only to OpenAI GPT, Claude, and Gemini models (other models draw on remaining credit); unused credits do not roll over; and the bonus percentages are tied to the plan tier. Whether the effective rate is "cheaper" depends entirely on your routing-group mix — the premium tier bills at 13% of list for gpt-5.6-sol, while the retryable award tier bills at 3%. We're deliberately not claiming across-the-board savings: rates, group availability, and offers (like the limited-time first-top-up multiplier) can change at any time.

For context on the models you'd be routing to, see our GPT-5.6 vs Claude comparison, the Grok 4.6 review, and the Gemini 3.7 Flash review.

Modelflare vs Alternatives

Three alternatives cover most of the same ground. Which one fits depends on whether you want a hosted discount gateway, self-hosted control, or observability depth.

• Modelflare vs OpenRouter: OpenRouter's catalog is far larger and it is the safer "default" choice for model variety. Modelflare's edge is price-tiered routing with published multipliers, native Anthropic/Gemini protocol support (so Claude Code and Gemini CLI connect natively), and per-tool integration guides. If you need obscure or niche models, OpenRouter wins; if you want cheaper access to the current flagship cohort with fallback, Modelflare is more focused.
• Modelflare vs LiteLLM: LiteLLM is the choice when self-hosting is non-negotiable — you keep your provider keys, data never leaves your infra, and the proxy is free. You pay for that with setup and maintenance burden, and usage dashboards you assemble yourself. Modelflare trades that control for a managed service; whether that trade is acceptable is a trust decision, not a technical one.
• Modelflare vs Portkey: Portkey leans into production observability — tracing, guardrails, caching, evaluations — with a hosted platform and an open-source gateway. Modelflare's logging is simpler: per-request status, latency, tokens, and cost. If you need deep tracing across complex agent pipelines, Portkey is the stronger observability layer; if you mainly need cost visibility and cheaper tokens, Modelflare covers it with less surface area.

Pros & Cons

Who Should Use Modelflare?

Who May Not Need Modelflare?

• Single-provider projects. If your app only calls one official API and you're happy with its console, a gateway adds a hop, a third party, and a billing layer without solving a problem you have.
• Compliance-constrained teams. No SOC 2 or ISO claims at the time of writing, and prompts transit a third party — regulated workloads may require certifications Modelflare doesn't yet advertise.
• Self-hosted requirements. If policy or paranoia says keys and traffic must stay on your infra, LiteLLM is the honest answer; Modelflare has no self-hosted option.
• Provider-specific feature depth. If you depend on provider-native features beyond the supported protocol surface (specific fine-tunes, org-level endpoints, or the newest model on day one), a gateway — any gateway — will lag the official API.
• Long-tail model hunters. 25 models covers the flagship cohort but not the long tail; OpenRouter's catalog is much larger.

FAQ

Final Verdict