Lists / inference hosting / Head to head
Together vs Fireworks (2026): What ChatGPT, Claude & Gemini Say
Together is named in 45 of 50 AI answers, Fireworks in 43. Together is named first 19 times, Fireworks 4. Model-by-model split, pricing models and when each fits.
AI models name Together slightly more often. In 50 recorded answers about inference hosting, Together is named in 45 and Fireworks in 43. On presence, that is close. The real gap is placement. Together is named first in 19 of 50 answers. Fireworks is named first in 4 of 50. Both appear in answers from all ten models.
This page counts what AI answers name. It does not judge either product’s quality.
TL;DR
- An AI shortlist will usually carry both. Every one of the ten models named each of them in at least three of its five answers.
- Together is the name the models lead with. Its 19 first mentions are the most of any product in the category, ahead of Hugging Face.
- Fireworks is named almost as often but rarely first. Its 78-point gap between named share and named-first share is the widest in the category.
- The OpenAI models name both least often. The Anthropic and Perplexity models name both in every answer.
- Morph’s pricing guide splits the choice by workload rather than naming one provider cheaper overall.
How often do AI models recommend Together and Fireworks?
Together is named in 45 of 50 answers and first in 19. Fireworks is named in 43 and first in 4. Hugging Face, the category leader, is shown as a reference row.
| Product | Named | Share | Named first | First share | Avg position | Category rank |
|---|---|---|---|---|---|---|
| Together | 45/50 | 90% | 19/50 | 38% | 1.78 | 2 |
| Fireworks | 43/50 | 86% | 4/50 | 8% | 2.56 | 3 |
| Hugging Face (category leader, reference) | 48/50 | 96% | 17/50 | 34% | 3.1 | 1 |
Share is the share of the 50 recorded answers that named the product. Named first is an answer where it appeared before any other tracked product. Average position is where the product sits in the answers that name it, so a lower number means earlier.
The named counts are nearly level. A buyer who asks any of these models about inference hosting will usually see both names. The difference is order. Together’s average position of 1.78 means it tends to be the first or second product an answer reaches. Fireworks sits at 2.56. Together’s 19 first mentions also exceed Hugging Face’s 17, even though Hugging Face is named in more answers.
Fireworks shows the opposite shape. Its named share is Fireworks 86% and its named-first share is Fireworks 8%, a 78-point gap. That is the widest gap of the 18 named products. The models treat Fireworks as a standard name on the list and rarely as the opening one. Together’s gap is 52 points. The full category record, with every answer, sits on the inference hosting index.
Which models prefer Together, and which prefer Fireworks?
No model names Fireworks more often than Together. Eight of the ten models name them equally often. GPT-5.6 Sol and Gemini 3.5 Flash name Together in more of their answers.
| Model | Family | Together | Fireworks |
|---|---|---|---|
| GPT-5.6 Sol | OpenAI | 4/5 | 3/5 |
| GPT-5.6 Terra | OpenAI | 3/5 | 3/5 |
| GPT-5.6 Luna | OpenAI | 3/5 | 3/5 |
| Claude Opus 5 | Anthropic | 5/5 | 5/5 |
| Claude Sonnet 5 | Anthropic | 5/5 | 5/5 |
| Claude Fable 5 | Anthropic | 5/5 | 5/5 |
| Gemini 3.6 Flash | 5/5 | 5/5 | |
| Gemini 3.5 Flash | 5/5 | 4/5 | |
| Sonar Pro | Perplexity | 5/5 | 5/5 |
| Sonar Reasoning Pro | Perplexity | 5/5 | 5/5 |
The sharpest split is by family. Across the three OpenAI models, the shares are Together 66.7% and Fireworks 60%. That is the thinnest coverage either product gets. The three Anthropic models and the two Perplexity models name both in every answer, Together 100% and Fireworks 100%. The two Google models sit between: Together 100% and Fireworks 90%, with Gemini 3.5 Flash the one Google model that skips Fireworks once.
Presence is only half of it. The OpenAI models name both less often, but two of them lead with Fireworks on price and speed. GPT-5.6 Terra and GPT-5.6 Sol both open their answer to the fast, cheap inference question with Fireworks AI. The Claude models lean the other way on order. Claude Opus 5 opens its list of main contenders with Together AI. Claude Fable 5 opens its managed-inference list with Together AI in its answer to the 2026 recommendation question.
Neither product is any single model’s most-named product. GPT-5.6 Sol names Modal most. The other nine models name Hugging Face most.
What do the answers say about each?
The models describe the two products as neighbours with different jobs. Five short extracts from the recorded answers, with markdown emphasis removed:
“Best overall for serving popular open-weight LLMs: Together AI” GPT-5.6 Sol, comparing the top options
“Best performance-focused managed inference: Fireworks AI” GPT-5.6 Sol, same answer
“Best default: Fireworks AI.” GPT-5.6 Terra, on fast, cheap inference
“Together AI and Fireworks AI are consistently top picks for teams that want production-grade hosted inference without touching GPUs.” Claude Sonnet 5, on a 2026 recommendation
“Best for Turnkey Serverless & Custom LoRAs: Fireworks AI or Together AI” Gemini 3.6 Flash, on a 2026 recommendation
The wording matches the counts. Together is framed as the general-purpose serving pick. Fireworks is framed as the performance and price pick. Several answers pair the two as one managed-inference option rather than as rivals.
How do Together and Fireworks differ?
They differ most on how dedicated compute is billed and on how much sits outside text inference. The panel does not measure pricing. The rows below are what the captured guides report, each marked to its source.
| Together | Fireworks | |
|---|---|---|
| Pricing model | Per-token or per-minute | Per-token or per-GPU-second |
| Dedicated GPU billing | Per hour, plus reserved raw GPU clusters | Per second, scales to zero on idle |
| Rate limits | Dynamic, no fixed published cap | A fixed published ceiling |
| Fine-tuned models | Fine-tuned weights can be downloaded | LoRA adapters served serverless on a shared base-model pool |
| Beyond text | Image, audio, rerank and a code interpreter on one bill | Text and vision serving plus embeddings and fine-tuning |
| Best for, per Northflank | Open-source model experimentation | Optimized inference APIs |
| Named for in the recorded answers | General-purpose serving of open-weight models | Performance-focused, fast and cheap inference |
One sourced fact on each side carries most of the decision. Together rents raw GPU clusters that Fireworks does not offer. Fireworks runs zero-data-retention on open models, with no prompt or generation logging without opt-in.
Northflank frames the core difference as optimization focus. Morph frames it as serverless model mix against owning a GPU footprint. The two guides describe the same split from different sides.
Who each is for follows from that. Morph calls Together’s single bill the simpler integration when a product spans modalities or needs sandboxed code execution next to inference. It calls Fireworks the tighter fit for a buyer who only serves text and vision.
When should you pick Together?
Pick Together if you want the product the models most often put first, or if your plans run past serverless calls into owned compute.
- Named first in 19 of 50 answers, more than any other product in inference hosting.
- Its average position of 1.78 is the earliest in the category.
- All three Anthropic models, both Perplexity models and both Google models name it in every answer.
- Morph reports that Together lets you download fine-tuned weights to move off-platform.
- For a broad model catalogue to experiment across, Northflank points to Together AI.
When should you pick Fireworks?
Pick Fireworks if your use case is serverless inference on price and speed, the question where two OpenAI models lead with it.
- GPT-5.6 Terra and GPT-5.6 Sol both open their fast, cheap inference answers with Fireworks AI.
- Every Anthropic and Perplexity model names it in all five answers.
- The answers cite fireworks.ai 48 times, second among vendor-owned hosts, so its own site feeds what the models read.
- Morph reports a fixed rate ceiling that teams can capacity-plan against.
- Where inference latency is the primary concern, Northflank points to Fireworks AI.
How this sits against the Together vs Fireworks guides
The ranking guides compare features, prices and speed. This page counts what AI answers name. Four of the ranked pages were captured.
Northflank. The page compares focus, deployment model, GPU support, fine-tuning and pricing model in one table. It is vendor-authored. It presents Northflank’s own platform as the alternative to both. Its catalogue row gives Together the larger model catalogue.
Price Per Token. The page is a model-by-model price table across the models both providers share. It lists Fireworks AI as cheaper on more of the shared models than Together AI. It also lists more models in total for Fireworks AI than for Together AI. That runs opposite to Northflank’s catalogue row, so catalogue size depends on whose count you read. No affiliate disclosure appears in the captured text.
Aditya Kamat on Medium. The post benchmarks one newly released model across Together AI, Fireworks and OpenRouter, using API credits on each provider. It reports fast response times for Together AI, and high long-output throughput with rate-limiting issues for Fireworks. It is a single-model test, not a platform review.
Morph. The page compares serverless, dedicated GPU, rate-limit, fine-tuning and compliance terms from each provider’s pricing page. It is vendor-authored. It promotes Morph’s own model hosting beside the comparison.
None of these pages reports what AI models say. That is the material this page adds: the named and named-first counts, the model-by-model split, and the recorded wording, all from one fixed sample.
How the sample was built
The sample is 10 models x 5 fixed prompts = 50 recorded answers, from the 2026-09 edition. Each model answered each prompt once, with web search on. The five questions, verbatim:
- What is the best platform to host and serve open-source models for a developer? Name specific products.
- Which platform to host and serve open-source models would you recommend to a developer in 2026?
- Compare the top platform to host and serve open-source models options right now.
- I’m a developer and I need a platform to host and serve open-source models. What should I use and why?
- Best platform to host and serve open-source models for fast, cheap inference?
The ten models by family: OpenAI (GPT-5.6 Sol, GPT-5.6 Terra, GPT-5.6 Luna, 15 answers), Anthropic (Claude Opus 5, Claude Sonnet 5, Claude Fable 5, 15 answers), Google (Gemini 3.6 Flash, Gemini 3.5 Flash, 10 answers) and Perplexity (Sonar Pro, Sonar Reasoning Pro, 10 answers). GPT-5.6 Terra, Claude Sonnet 5 and Gemini 3.6 Flash fill the panel’s ChatGPT, Claude and Gemini slots. The panel counts Together when an answer says “Together AI” or “Together”, and Fireworks when it says “Fireworks”. The full method is on the method page.
What these counts cannot tell you
These counts measure presence in AI answers. They say nothing about uptime, speed, support, price or fit with your stack. Being named is not the same as being recommended, since an answer can name a product only to warn against it. Each model answered each question once, so a model’s split rests on five answers. The figures are one dated snapshot. Aliases are matched as strings. The answers come from model APIs, which can differ from consumer chat apps, and every prompt was in English. Citation counts reflect the citations returned in the recorded responses, and coverage varies by model.
Frequently asked questions
Is Together or Fireworks cheaper?
It depends on the workload. Morph reports Fireworks cheaper or tied on the highest-volume serverless models. The same page reports Together cheaper on dedicated GPUs. Price Per Token lists Fireworks AI as cheaper on more of the shared models. The panel does not measure price, so check both price pages against your own model mix.
Which one do AI models suggest for fast, cheap inference?
Fireworks is the one two OpenAI models lead with on that question. GPT-5.6 Terra and GPT-5.6 Sol both open their answer with Fireworks AI. Across all five questions, Together is still named in more answers, 45 of 50 against 43.
Can you switch between Together and Fireworks later?
Morph reports that both expose OpenAI-compatible endpoints, so migrating between them is mostly a base-URL and API-key change. Northflank also lists OpenAI-compatible APIs among Together AI’s strengths.
Which is faster, Together or Fireworks?
The panel does not measure speed. The one captured benchmark tested a single model and reported fast response times for Together AI and high long-output throughput for Fireworks. The same post reports rate-limiting issues for Fireworks.
Can Together or Fireworks pay to change these counts?
No. No position is sold, sponsored or influenced, and vendors cannot pay to appear, be reordered or be removed. The counts change only when a new monthly edition records new answers.