TL;DR: Choosing the right Claude model comes down to four current options: Haiku 4.5, Sonnet 5, Opus 5, and Fable 5, each optimized for a different balance of speed, cost, and capability. Pricing spans a tenfold range, from $1 per million input tokens on Haiku 4.5 to $10 per million input tokens on Fable 5. For most day-to-day coding, writing, and analysis work, Sonnet 5 is the model built to balance speed and intelligence.
Key terms
● Claude Haiku 4.5*: Anthropic's fastest current model, priced for high-volume and latency-sensitive work.
● Claude Sonnet 5: the model Anthropic positions as the best combination of speed and intelligence, and the default on the Free and Pro plans.
● Claude Opus 5: the model built for complex agentic coding and enterprise work, and Anthropic's default recommendation when a team is unsure which model to pick.
● Claude Fable 5: Anthropic's most capable generally available model on paper, though Opus 5 matches or beats it on several benchmarks at a fraction of the price. It's worth reserving for the narrowest, most demanding agentic tool tasks.
*Haiku's version number trails the others because Anthropic ships each model tier on its own release schedule, not as a synchronized generation. A "4.5" and a "5" released months apart aren't a hierarchy signal, just different points on each tier's own timeline.
The right Claude model depends on what the task demands. A quick extraction job and a multi-step coding agent need different levels of reasoning, and defaulting to the same model for both means overpaying for one or underpowering the other. That calculation got more complicated the day Anthropic shipped a fourth tier above Opus. Today, a team must weigh four models across three variables:
- how capable the output needs to be
- how fast the response has to land
- what the token bill can absorb
Get the pairing wrong, and it costs you either way. Send a complex task to a cheap model, and it may stall or need retries, burning more time and tokens than starting with the right model would have. Send a simple task to an expensive model, and you pay premium rates for work a cheaper model would have handled just as well.
In this piece, we examine the differences between Haiku, Sonnet, Opus, and Fable, then break down which one best fits coding, writing, and cost-sensitive production work.
What's the difference between Claude Haiku, Sonnet, Opus, and Fable?
The four models sit on a single ladder of capability, speed, and price. The trade-off runs in a predictable direction: the more capable the model, the higher its per-token cost and the slower its typical response time.
Source: Anthropic's Claude models overview, current as of publication.
That context-window column matters more than it looks. Haiku 4.5's 200K-token window covers most single-request tasks, a support ticket, a short document, a focused coding question, and keeping that window smaller is part of what makes Haiku fast and cheap. Sonnet 5, Opus 5, and Fable 5 share a 1M-token window instead, built for a different kind of job: holding an entire large codebase, a lengthy contract, or a full research corpus in context at once, without chunking it into pieces first.
One detail worth noting before comparing use cases: Opus 5 has the most recent reliable knowledge cutoff of the four, May 2026, ahead of both Sonnet 5 and Fable 5. Fable 5 shipped in June 2026, and Opus 5 followed six weeks later with a more recent training snapshot. A model's position on the capability ladder and how current its knowledge is are two different things, so it's worth checking both before assuming the priciest option always knows the most.
Which Claude model should you use if you're not sure?
Anthropic documents two starting strategies, and the right one depends on which mistake costs you more: underpowering a hard task, or overpaying for an easy one.
The default is capability-first: start with Opus 5, since it covers the widest range of serious work, coding, enterprise workflows, vision-heavy tasks, and computer use, without the premium pricing of Fable 5. Test the workflow, then move down to a cheaper model once it's proven. This is the right starting point whenever you're unsure, since it fails safe: the worst case is paying slightly more than necessary, not shipping a task the model couldn't handle
Reach for Fable 5 only for the narrow set of tasks that specifically warrant it, the most complex, long-running work for autonomous agents. For nearly everything else, including problems that feel like they need the top tier, Opus 5 tends to match it on performance for less. Anthropic frames Fable 5 as next-generation intelligence for long-running agents, not as a default upgrade path from Opus.
The alternative is efficiency-first: start with Haiku 4.5 for cost-sensitive or high-volume work, test thoroughly, and upgrade only where a real capability gap shows up. This fits when the task is likely simple, high-volume, or latency-sensitive, cases where the risk of overpaying matters more than the risk of underpowering.
Which Claude model is best for coding?
For most day-to-day coding work, Sonnet 5 is the starting point. Anthropic presents it as frontier intelligence built for coding, agents, and enterprise workflows, closing much of the gap with Opus-class models at a fraction of the price.
Complex agentic coding changes the approach. Multi-hour autonomous coding agents, large-scale refactoring, and complex systems engineering are the specific use cases Anthropic points to for Opus 5, and it’s a step-change improvement over its predecessor for long-horizon, high-autonomy work.
Tuning effort up or down within Opus 5 or Sonnet 5 often solves a coding performance problem faster than switching models, since effort tuning trades intelligence off against latency and cost within the same model. On Opus 5, the default effort level is a reasonable starting point, with room to step up for the most demanding agentic work.
Which Claude model is best for writing?
Sonnet 5 is the strongest default for most writing work: long-form content, editing, structured documents, and research synthesis. All of that falls within the coding, agents, and enterprise workflows Anthropic built it around, and speed matters more for iterative writing than for a one-shot coding task.
Fable 5 delivers nuance and sustained coherence across long or complex pieces, technical documentation with dense references, and content that needs to maintain a large amount of context over many pages.
What are the best use cases for Claude Haiku 4.5?
Haiku 4.5 is built for real-time, high-volume workloads that still call for real reasoning, not a scripted response, without the cost of a larger model. Think customer-facing chat that needs to respond in under a second, or classification and tagging run across millions of records. It also fits well as a sub-agent within a larger multi-model pipeline, handling routine steps so that a more expensive model is called only when needed.
The trade-off: Haiku 4.5's reliable knowledge cutoff is February 2025, which is noticeably older than the other three current models. For work that depends on recent events, current pricing, or anything that's changed in the last year and a half, that gap can outweigh the speed and cost savings.
What are the best use cases for Claude Opus 5 and Claude Fable 5?
Opus 5 and Fable 5 both sit at the top of the lineup, but the performance gap between them is smaller than the price gap suggests. Anthropic's own benchmark data shows Opus 5 matching or beating Fable 5 on several published evaluations, including an outright win on computer-use tasks, at roughly a third to half the cost. For most complex work, enterprise workflows, knowledge work, and vision-heavy tasks, Opus 5 usually delivers the stronger price-to-performance result, even on tasks that feel like they should need the top tier.
Fable 5 rarely justifies its premium outside a narrow slice of work. Anthropic still points to it specifically for long-running agentic tasks, deep reasoning, and advanced research, the cases where getting the answer right matters more than getting it cheaply. Starting with Opus 5 isn't just the cheaper default. On most of what a team actually builds, it's also the better one.
How do you manage costs across different Claude models?
The biggest lever is matching effort to the task, then combining models so only the hardest work runs on the expensive one. There are two patterns:
- The executor model handles routine work and escalates difficult decisions to a more senior advisor model. For example, Haiku 4.5 can take the bulk of requests and then escalate exceptions to Sonnet 5 or Opus 5.
- The executor model handles routine work and escalates difficult decisions to a more senior advisor model. For example, Haiku 4.5 can handle the bulk of requests and then escalate exceptions to Sonnet 5 or Opus 5.
- The orchestrator delegates bulk work to lower-cost workers while keeping a frontier model, such as Opus 5 or Fable 5, for coordination.
Plan tier matters too. Sonnet 5 is the default model on Claude's Free and Pro plans, with Opus 5 available as the strongest option on Pro and the default on Max. Fable 5 sits above both and is available on paid plans for work that specifically needs it. Before scaling any workload, a benchmark test against the actual use case is the most reliable way to confirm whether a cheaper model clears the bar.
Choosing a Claude model is a routing decision
The uncomfortable truth about choosing a Claude model is that most teams working with Claude aren't using just one. They're routing: Haiku 4.5 for high-volume steps, Sonnet 5 for daily coding and writing work, and Opus 5 or Fable 5 for specific tasks that require that level of capability. Treating “which model should we use” as a single, permanent decision usually means overpaying for routine work or underequipping the hard problems, sometimes both at the same time.
That routing decision becomes easier with a real evaluation set rather than a hunch. Svitla works with teams on AI strategy and implementation, helping you build on Claude, choose the right model mix, and put the effort and cost controls in place before the bill arrives as a surprise.
FAQ
Is Claude Haiku 4.5 good enough for production applications?
Yes, for the workloads it's built for. Haiku 4.5 delivers near-frontier intelligence at the fastest speed and lowest cost in the current lineup, which makes it a legitimate production choice for real-time applications, high-volume processing, and sub-agent tasks rather than just a prototyping tool. The main constraint is its older knowledge cutoff, worth checking against any workload that depends on recent information.
How much more expensive is Claude Opus 5 than Claude Sonnet 5?
Opus 5 is priced at $5 per million input tokens and $25 per million output tokens, compared to $2 and $10 for Sonnet 5, roughly 2.5 times the cost on both input and output. The jump from Opus 5 to Fable 5 is a similarly sized price increase, Fable costs $10 and $50, roughly double Opus's rate. What doesn't scale the same way is capability: Opus 5 already matches or beats Fable 5 on several published benchmarks, which makes the Sonnet-to-Opus jump the one more likely to be worth paying for.
Can I switch Claude models without losing my conversation or project context?
Yes. Model choice is independent of your conversation history and project files in claude.ai, so switching models mid-project doesn't reset what Claude already knows about the conversation. That makes it practical to start a task on a cheaper model and escalate to a more capable one only if it turns out to need it.
Do all current Claude models support the same core features?
Mostly. All four current models support text and image input, text output, multilingual capabilities, and vision. Where they differ is in reasoning mechanics: Sonnet 5, Opus 5, and Fable 5 use adaptive thinking, with Fable 5 running it permanently on, whereas Haiku 4.5 uses a fixed extended-thinking setup. That difference affects how each model trades reasoning depth for speed.
What's the fastest way to figure out which Claude model fits a new task?
Pick a starting point based on the task, then test. For cost-sensitive or high-volume work, start with Haiku 4.5 and upgrade only if a specific capability gap shows up. For complex reasoning or advanced coding, start with Opus 5 and move down to a cheaper model once the workflow is proven. Either way, a benchmark built from real prompts and data beats guessing from a spec sheet.
Where can Claude models be accessed beyond claude.ai?
Claude models are available through the Claude API, Amazon Bedrock, Claude Platform on AWS, Google Cloud, and Microsoft Foundry, which matters for teams building Claude into a production system rather than using it as a chat interface. Svitla's AI and machine learning team works across these deployment paths to help teams integrate the right Claude model into an existing enterprise stack.
