Two models, one list price 20 times the other, and a benchmark table where the cheaper one takes a category. Picking between Claude Haiku 5.5 and GPT-6.1 Sol on a single number is how a wrong call gets made.
The workable approach is to break the decision into primitives and evaluate each one separately. Unit price per million tokens. Tier threshold. Tokens a task actually consumes. Reasoning effort. Migration cost. The sections below go through them in that order.
The Two Models and Their Design Intent
Claude Haiku 5.5
Anthropic released Claude Haiku 5.5 on 2026-10-07 with the model identifier claude-haiku-5-5. In Anthropic's product ladder, the Haiku line has carried high-throughput, low-latency baseline tasks, and 5.5 changes more than previous iterations did.
It is the first release in the series to support custom reasoning effort, with low, medium, high, xhigh and max settings and a default of medium. Logic-heavy research report analysis can run at a higher effort in exchange for a higher reasoning accuracy rate. Pure text labeling or customer service triage can run lower to compress compute latency and cost.
Vision and action training for desktop operating system interface recognition and browser operation is part of this release, and it shows up as a jump on computer-use benchmarks such as OSWorld. Anthropic positions Haiku 5.5 as the primary assisting subagent for Opus 5.5 and Sonnet 5.5. In a complex software engineering pipeline, Opus or Sonnet sets the top-level plan while Haiku 5.5 goes into the codebase to locate symbol definitions, read documentation and extract variables, sending results back at more than 240 tokens per second.
The tokenizer is a new generation, similar to the one used in Sonnet 5.5. According to Anthropic's documentation, on the same plain text content it typically produces about 30% more tokens than Haiku 4.5. Pricing is tiered with 100K tokens as the cutoff point, and rates rise considerably in long-context scenarios.
GPT-6.1 Sol
OpenAI launched GPT-6.1 Sol on 2026-09-29 with the model identifier gpt-6.1-sol. In the GPT-6 product matrix, Luna covers ultra-low-cost and high-volume invocation, Astra covers ultra-high-spec frontier exploration, and Sol carries the backbone role for the majority of enterprise production systems.
Compared with earlier GPT-6 models, Sol improves execution coherence on complex long-chain tasks, and its knowledge base is updated through the end of April 2026. It defaults to medium reasoning effort and can unfold an implicit thinking process according to prompt difficulty, which gives it fault tolerance in complex algorithm implementation, code refactoring and cross-module dependency analysis.
The interface conventions changed as well. Sol removed tool calling from the standard Chat Completions API. Calling external functions, searching the web and operating on structured data all require the Responses API. Parameter accuracy on multi-stage function calls improves, and the adaptation cost of migrating legacy systems goes up.
Sol also widens the standard low-rate range to 272K tokens, against the 100K warning line on Haiku 5.5. A request carrying a large amount of code, several technical specifications or a long conversation history follows a flatter price curve.
Specs and Token Pricing
Context window and output limits
| Item | Claude Haiku 5.5 | GPT-6.1 Sol |
|---|---|---|
| Release date | 2026-10-07 | 2026-09-29 |
| Context window | 1M tokens | 1.05M tokens |
| Max output | 128K tokens | 128K tokens |
Standard text token pricing
USD per million tokens:
| Tier | Input | Cache read | Cache write | Output |
|---|---|---|---|---|
| Haiku 5.5, prompt ≤100K | 0.10 | 0.01 | 0.125 | 0.50 |
| Haiku 5.5, prompt >100K | 0.50 | 0.05 | 0.625 | 2.50 |
| Sol 6.1, prompt ≤272K | 2.00 | 0.10 | 2.50 | 10.00 |
| Sol 6.1, prompt >272K | 4.00 | 0.20 | 5.00 | 15.00 |
The tier is charged against the whole request
Both vendors price their tiers against the entire request. When a Haiku prompt goes from 100,000 tokens to 100,001 tokens, the rate moves up for the whole request rather than only for the extra token. Long system prompts, tool definitions and accumulated conversation history can each push a request over the line.
The tokenizer change adds a second effect. Anthropic notes that the same text yields roughly 30% more tokens on Haiku 5.5 than on Haiku 4.5, so budgets need to be re-counted on the new model instead of carried over from old figures.
Cost per Request, Worked Through
The table below uses fixed input and output token counts with the pricing above. It covers input and output only, with no tool fees, retries or taxes.
| Input / Output | Haiku 5.5 | Sol 6.1 | Sol as a multiple of Haiku |
|---|---|---|---|
| 5K / 1K | $0.001 | $0.020 | 20x |
| 100K / 2K | $0.011 | $0.220 | 20x |
| 150K / 5K | $0.0875 | $0.350 | 4x |
| 300K / 5K | $0.1625 | $1.275 | ~7.8x |
| 900K / 10K | $0.475 | $3.750 | ~7.9x |
Scale the small case up
A request with 5K input and 1K output, run 100,000 times, comes to about $100 on Haiku 5.5 and about $2,000 on Sol. When the prompt lands between 100K and 272K, the gap narrows to roughly 4x.
What the table leaves out
This compares token unit prices only. For the same task, the two models consume different numbers of tokens, and the FrontierCode data further down shows how far apart those totals can be.
Benchmarks: Reading the Numbers Correctly
Data sources are not identical. Most of Haiku 5.5's scores come from Anthropic's release materials and third-party evaluations by Artificial Analysis (AA), while Sol's scores come from AA and public leaderboards. Reasoning effort, harness and scoring method differ from test to test, so these rows are not a like-for-like comparison under identical conditions.
Knowledge work
| Test (Elo) | Haiku 5.5 (max) | Sol 6.1 (max) | Haiku 5.5 (medium) |
|---|---|---|---|
| GDPval-AA v2.1 | 1620 | 1575 | 1277 |
| AA-Briefcase v1.1 | 1578 | 1564 | 1372 |
At the highest reasoning effort, Haiku 5.5 leads by 45 and 14 Elo points. Elo is a relative score, and a 14-point gap is not enough to show a meaningful difference in actual performance.
At the default medium effort, Haiku 5.5's scores drop to 1277 and 1372. AA reports a weighted task cost of about $0.047 at medium and about $0.213 at max, roughly 4.5x. The 1620 figure therefore belongs to a high-cost configuration, not to default usage.
Terminal coding
| Test | Haiku 5.5 | Sol 6.1 |
|---|---|---|
| Terminal-Bench 4.0 (public leaderboard and Anthropic's own testing) | 39.2% | 58.2% |
| Terminal-Bench 4.0 (AA, max) | 32.8% | 56.1% |
Both datasets point the same way, with Sol ahead by 19 to 23 percentage points. Haiku's 39.2% comes from Anthropic running it in Claude Code, while Sol's 58.2% comes from the public Codex leaderboard, so the harnesses differ. Anthropic itself states that Sonnet 5.5 and Opus 5.5 are better suited to complex agentic coding, while Haiku 5.5 suits narrowly scoped tasks.
Repository tasks (FrontierCode 1.1 Main)
| Item | Haiku 5.5 | Sol 6.1 |
|---|---|---|
| Score | 46.4% | 50.2% |
| Pass rate | 51.6% | 55.8% |
| Best effort | max (Claude Code) | medium (Codex) |
| Average cost per run | $1.33 | $0.36 |
| Average output tokens | 181.4K | 11.4K |
This dataset is the most telling one. Haiku 5.5 has the lower unit price, but at max effort it outputs about 180K tokens, so its cost per run is roughly 3.7x that of Sol at medium. A low price per token does not automatically produce a low cost for finishing the task.
Computer use (OSWorld 2.1 offline subset, max)
| Metric | Haiku 5.5 | Sol 6.1 |
|---|---|---|
| Partial score | 72.4% | 76.6% |
| Full pass rate | 37.1% | 39.8% |
These are Anthropic's results on the same batch of 82 tasks, with each task run 5 times, so they are vendor self-reported. The gap between the two models is small, and neither reaches a 40% full pass rate. A high partial score does not mean the task was actually completed. Offline tasks also do not represent logins, network errors or content changes on real web pages.
Composite index, reasoning and speed (AA, max)
| Item | Haiku 5.5 | Sol 6.1 |
|---|---|---|
| Intelligence Index v4.3.2 | 43.4 | 51.8 |
| Humanity's Last Exam (no tools) | 44.4% | 52.9% |
| GDP.pdf (all passed) | 20.8% | 31.0% |
| AA-LCR v1.1 (long context) | 82.7% | 83.0% |
| Output speed (tokens/sec) | 241.9 | ~55 |
| Weighted task cost | $0.213 | $0.724 |
Sol leads on the composite index and on the reasoning tests. On long-context retrieval (AA-LCR) the two are almost even. Haiku generates about 4.4x faster than Sol, and its weighted task cost is about 29% of Sol's.
One caveat applies to speed. Generation speed is only part of latency. Thinking time, tool execution and retries all affect how long a user actually waits, so the measurement that matters is the one taken on your own tasks at the target effort level.
Integration and Migration Boundaries
GPT-6.1 Sol and the Responses API
Tool calling on Sol must go through the Responses API, since the Chat Completions API does not support tools. An existing Chat Completions application has to change its API at the same time it changes models.
Claude Haiku 5.5 and migration from 4.5
Moving from Haiku 4.5 involves several changes. Anthropic's migration guide adjusts the thinking configuration, removes assistant prefill, restricts some sampling parameters and replaces the toolset for computer use. The safety classifier may return a refusal outright, with no server-side fallback. Haiku 5.5 does not yet support Priority Tier.
Migration cost belongs in the decision as well. For an application that is running stably, switching models for a marginal improvement can cost more in retesting than the token savings it produces.
A Selection Table
| Use case | Recommendation | Basis |
|---|---|---|
| Classification, routing, fixed-format extraction | Try Haiku 5.5 first | Lower unit price, faster |
| Summarization and document processing under 100K | Try Haiku 5.5 first | Knowledge-work score is close to Sol |
| Difficult terminal and repo tasks | GPT-6.1 Sol | Clear lead on Terminal-Bench |
| Screenshot-driven computer use | Test both | Small gap, and the full pass rate is low for both |
| Repeated long prompts from 100K to 272K | Compare Luna and Sol | Haiku's unit price goes up 5x in this range |
Subscription plans are a different comparison
Everything above is token-based API pricing, and subscription plans do not compare the same way. SemiAnalysis tested various subscriptions with agent workloads and converted the quotas into API-equivalent prices, concluding that a Claude subscription running Opus 5.5 is worth roughly 5x an OpenAI plan of the same tier running GPT-6.1 Sol. The direct reason is that OpenAI halved the token quotas for models at every tier of its $200 plan.
About 96.6% of the tokens in that test came from cache reads, which is closer to how agents repeatedly reuse context. Ordinary chat users may not get the same converted value. SemiAnalysis also estimates that subscriptions make up only about 10% of Anthropic's revenue while potentially consuming more than 40% of its inference compute. How heavily subscription quotas are subsidized shifts with each vendor's strategy, so depending on a single subscription over the long term carries uncertainty.
Why per-task routing is hard to do by hand
After looking at the numbers for both models, one practical problem stands out. Task difficulty varies enormously within a development team. Cleaning up meeting notes, triaging tickets and extracting fields waste the most expensive model. Refactoring a core module or tracking down a complex dependency problem makes a cheap model fail again and again.
The community discussion around this tends to split into two views. One is that not every task needs the most expensive model, and that spending a top-tier model on simple tasks does not pay off when the budget is limited. The other is that having a person judge the difficulty of each task one by one is not realistic, and that dropping to a lower-tier model creates a noticeable drop in experience, so automatic routing ends up being necessary anyway.
Both views hold. A more workable arrangement is to fix the division of labor by project and scenario first, letting lightweight tasks default to a low-cost model while important tasks go to a high-performance one, and then to manage keys, channels and switching from a unified entry point.
Running Both Models Behind One Entry Point
For teams that arrive searching for a cc-switch alternative, the useful framing is architectural rather than a feature list. A switcher manages which provider is currently active. A gateway manages how traffic moves between providers, which is the part that has to keep working while a long task is in flight.
What a gateway handles
ServBay is an AI development management tool, and it ships with an AI gateway that puts multiple model sources behind a single entry point.
- Sources. Official AI APIs from various vendors, subscription accounts and relay providers can all be added.
- Traffic scheduling. Traffic can be distributed automatically, with hot channel switching and automatic failover, plus usage statistics. When a channel becomes unavailable, requests route to a backup channel automatically.
- Channel priority. Priorities decide which channel is used first, so lower-cost channels can serve as the default and high-performance channels as a supplement.
- Model mapping. Model names hardcoded in an application can be mapped to the model actually used, for example claude-opus-5 mapped to glm-5.2. A project whose tasks are mainly summarization and classification can have the model name it calls mapped to Claude Haiku 5.5, with no change to the application code.
- Local virtual keys. Multiple virtual keys can be created and assigned to different development projects. Project A's key maps to Haiku 5.5, Project B's key maps to GPT-6.1 Sol, and each project's usage can be tracked separately.
- Protocol conversion. Whether the upper-layer application uses the OpenAI, Anthropic or Gemini protocol, ServBay converts it to the protocol the underlying channel requires. A tool that only supports the OpenAI protocol can still call Anthropic models, so developers do not have to adapt each one individually.
A configuration for these two models
Everyday summarization, classification and subagent tasks point to Haiku 5.5. Complex repository and terminal tasks point to GPT-6.1 Sol. Once a Haiku 5.5 project goes past 100K tokens, its unit price rises, so that project gets an extra backup channel. When a model version is updated, the mapping is the only thing to change, not the code in each project.
FAQ
Which is better, Claude Haiku 5.5 or GPT-6.1 Sol?
There is no single answer. Haiku 5.5 is cheaper and faster, and its knowledge work score is close to Sol's. Sol has a clear lead in terminal coding and on the composite reasoning index. The task type decides.
How much does Claude Haiku 5.5 cost?
For prompts up to 100K tokens, it costs $0.10 per million input tokens and $0.50 per million output tokens. Above 100K, those rates become $0.50 and $2.50.
How much does GPT-6.1 Sol cost?
For prompts up to 272K tokens, it costs $2 per million input tokens and $10 per million output tokens. Above 272K, those rates become $4 and $15.
Can Haiku 5.5 replace Sol for coding?
For simple, well-scoped editing tasks, yes. For complex terminal and repository tasks, the available data supports Sol. Anthropic also recommends Sonnet 5.5 or Opus 5.5 for complex agentic coding.
Do the two models have the same context window?
They are close. Haiku 5.5 has 1 million tokens, and Sol 6.1 has 1.05 million tokens, and both have a maximum output of 128K tokens. Different vendors tokenize differently, so the same text converts to different token counts.
Can one project use multiple models at the same time?
Yes. By configuring multiple channels and model mappings through an AI gateway, the application only needs to connect to a single entry point, and the gateway decides which model is actually called.
Takeaways
The differences between Claude Haiku 5.5 and GPT-6.1 Sol show up mainly in three areas. A 20x price gap. A clear lead for Sol in terminal coding. The two being close in knowledge work and computer operation. The total cost of completing a task also depends on reasoning effort and token consumption, so judging by unit price alone can easily lead to a wrong call.
In practice, splitting work by task type is more reasonable than settling on a single model. Teams that need to manage multiple models and channels at the same time can route them through an AI gateway, allocating keys per project and setting priorities and model mappings, which moves the decision of which model to use into the configuration layer while application code stays unchanged.
Top comments (0)