Together AI vs Claude: open models or closed in production

If you have shopped for inference at any point in 2026, you have probably compared codexkey and Together.ai. On the surface they look like rivals: both offer an OpenAI-compatible HTTP API, both bill per token, both promise low latency and high throughput. Underneath, they solve very different problems and carry very different model catalogs.

codexkey is a Claude-focused gateway: Sonnet and Opus from the Anthropic family, plus gpt-5.6-luna for cheap workloads, with prompt caching, tool use and EU/RU payment rails. Together.ai is the largest commercial host of open-weight models in the world: Llama 3.3, Qwen 2.5, DeepSeek-V3, Mixtral and a long tail of fine-tunes at aggressive per-token prices.

We do carry Haiku: the catalog lists claude-haiku-5.5 and claude-haiku-4.5, with prices on the models page. They run in the Premium channel. The Cheap channel has no Haiku.

This is the honest version of the comparison. Where do open weights at scale beat the proprietary frontier? Where does Claude still lead? And, spoiler, why most production teams in 2026 end up running both.

What each service promises

Together.ai rests on the thesis that open-weight models have caught up "well enough" for most workloads, and that the winning move is to host them cheaper, faster and more reliably than you could yourself. Llama 3.3 70B, Qwen 2.5 72B, DeepSeek-V3 (671B MoE) and dozens of smaller models sit behind one OpenAI-compatible endpoint. Dedicated endpoints, fine-tuning and batch inference are on offer too. The pitch: pay 5-10× less than for frontier closed models and get 80-90% of the capability.

codexkey rests on a different thesis. For the workloads that really matter (agentic coding, long-context reasoning, reliable tool use, customer-facing assistants), the gap between Claude and the best open model is still significant. And teams in the EU and RU should not have to fight Stripe declines, sanctions friction or US-only billing to reach it. The pitch: frontier Claude through an OpenAI-compatible API, with SBP, cards and crypto on the payment side and prompt caching on the cost side.

Different problems, different answers. The question is not "which is better" but "which one for which task."

Price per quality: the math everyone wants

Let's use concrete 2026 numbers. Approximate prices per million tokens:

Two things stand out:

1. Open-weight output tokens are 5-17× cheaper than Sonnet's: open models are usually priced symmetrically (input ≈ output), while Claude charges a premium for generation.
2. Quality on hard reasoning is close but not equal. On SWE-Bench, on long-context retrieval, on agentic tool loops, Sonnet 4.6 still leads Llama 3.3 70B by 10-20 percentage points, and keeps a smaller but real edge over DeepSeek-V3.

For classification, extraction, summarization and template generation, that quality gap does not turn into a product difference, but the price gap does. For a coding agent that has to land a multi-file refactor on the first try, the quality gap shows up at once: wasted iterations, broken builds, engineering time lost. All of it outweighs what you save on inference.

When open weights win (use Together)

Some workloads are a straight match for Together.ai:

• High-volume classification. Tagging support tickets, routing email, scoring leads, moderating UGC. Millions of calls a day, short prompts, a need for cheap output and predictable latency. Llama 3.3 70B at sub-dollar prices wins on TCO.
• Embeddings and retrieval-adjacent generation. Synthetic queries, search-term expansion, rewriting documents for the index. Quality hits a plateau quickly here.
• Fine-tuning for a narrow domain. If you have proprietary data and one specific task (legal clause extraction, medical coding, niche translation), a fine-tuned Llama 3.3 8B on Together can beat a generic frontier model and cost 50× less per call.
• A hedge for self-hosting. If you ever have to bring the model in-house (regulation, data residency, cost), open weights leave that exit open. Closed models do not.
• Batch and overnight pipelines. Together's batch API is about 50% cheaper again, which suits nightly enrichment jobs that do not need real-time latency.

If your workload reads "I need a competent LLM called 50 million times this month, and I do not need the absolute best answer," Together does exactly the job it was built for.

When Claude wins (use codexkey)

Then there are the tasks where the price-per-quality math flips:

• Coding agents. Cursor, Cline, Aider, Claude Code and the rest of the agentic-coding ecosystem default to Claude, and not by accident. Sonnet's tool-use reliability, file-edit precision and multi-step planning still have no equal in production. A failed agent turn costs more than a successful one because of the retry loop.
• Complex multi-step reasoning. Strategy memos, architecture design, legal analysis, anything where the chain of reasoning matters. Sonnet 4.6 is more likely to give a correct, internally consistent answer on the first pass.
• Long context with caching. Claude's 200K context plus Anthropic's prompt caching makes "load a 100-page document once, ask 50 questions" cheaper than on most open models, because cached tokens cost a fraction of fresh input.
• Reliable tool use at depth. Open models can call tools, but they have failure modes: wrong arguments, invented function names, infinite loops. These appear at depth 3+ and are painful to debug.
• Customer-facing assistants where tone and refusal behavior matter. Claude's safety and quality tuning is more predictable in production. Fine-tuned open models can drift in unwelcome directions.

If the task reads "this answer goes to a paying customer, or gets committed to a codebase, or drives a decision," the cheapest token is rarely the right one.

Payments: where the EU/RU difference is real

Together.ai bills in USD through Stripe. For a US or western European company with a corporate card, that is a non-issue. If you are a developer in Russia, Belarus, Kazakhstan, parts of MENA, or even some EU jurisdictions where Stripe gets nervous about your MCC code, you have probably met declined payments already.

codexkey accepts cards, SBP (the Russian Faster Payments System) and crypto through CryptoBot, Platega and CryptoCloud. For a Moscow startup or a Belgrade studio, "the API works and the payment goes through" is a feature in itself.

This is not a small thing. We have spent enough hours working out "why does Stripe think my perfectly legitimate Russian-issued card is fraud" to know that the usability of payment rails is a real moat.

Code: switching providers with the OpenAI SDK

Both services are OpenAI-compatible. Switching takes one line:

from openai import OpenAI

# codexkey (Claude Sonnet 4.6)
codexkey = OpenAI(
    api_key="sk_cdx_...",
    base_url="https://api.codexkey.online/v1",
)

# Together.ai (Llama 3.3 70B)
together = OpenAI(
    api_key="...",
    base_url="https://api.together.xyz/v1",
)

def smart(prompt: str):
    return codexkey.chat.completions.create(
        model="claude-sonnet-5",
        messages=[{"role": "user", "content": prompt}],
    )

def cheap(prompt: str):
    return together.chat.completions.create(
        model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
        messages=[{"role": "user", "content": prompt}],
    )

def route(task: dict):
    if task["type"] in {"classify", "extract", "summarize_short"}:
        return cheap(task["prompt"])
    return smart(task["prompt"])

That is the whole pattern: one client per provider, one routing function, done.

Can you use both? Yes, and you probably should

The strongest 2026 production stacks are not "codexkey OR Together" but "codexkey AND Together," routed by task type:

• Classification / extraction / batch enrichment → Together (Llama 3.3 70B or Qwen 2.5 72B)
• Search query expansion / synthetic data → Together
• Coding agents / reasoning / customer-facing assistant → codexkey (Sonnet 4.6)
• Cheap drafts that a smarter model will refine → Together (Llama 3.3 8B) → codexkey (Sonnet) for the polish
• Long-document Q&A → codexkey with prompt caching

A simple router that looks at the task type and the expected output volume can cut 60-80% of inference cost compared with "everything goes to Sonnet" and lose almost nothing in product quality, because the tasks sent to the cheap model are exactly the ones where the quality gap does not matter.

For prompt caching strategy and the cost math on Claude, read Claude API Pricing in 2026.

Bottom line

Together.ai and codexkey are not rivals but complementary halves of a healthy production LLM stack.

• Together: for high-volume, quality-tolerant, cost-sensitive workloads. Open weights, batch, fine-tunes, the long tail of models.
• codexkey: for tasks where Claude's frontier capability really shows in the product (coding, reasoning, long context, tool use, customer-facing flows) and where EU/RU payment rails matter.

By default, send the intelligence that faces your users to codexkey. Send the grunt work to Together. Route by task, not by feel. Your bill and your quality metrics will both thank you.