Claude Haiku 5.5 vs Ling 3.1 Flash: A 560-Billion-Parameter Model That Costs Four Times More Per Answer

Six days separate them. InclusionAI shipped Ling 3.1 Flash on October 1, 2026; the vendor shipped Claude Haiku 5.5 on October 7. Both are sold as flash-tier models, both carry a one-million-token context window, and on the reasoning rows of the one independent board that has run both they are so close that the difference is inside the noise. Then you get to the row that measures whether an agent actually finishes the job, and they are 26 points apart — and to the row that measures what a finished job costs, where the model with 560 billion parameters is four and a half times the price of the one whose parameter count nobody has published.

Neither rate card predicts that. Ling 3.1 Flash lists at $0.30 per million input tokens and $0.90 per million output tokens. Claude Haiku 5.5 lists at $0.10 and $0.50 for prompts up to 100,000 tokens, stepping to $0.50 and $2.50 above that. Read as a per-token price, Ling is three times the cost on input and a little under twice on output, which is the kind of gap that ends a comparison in one line.

It does not end this one, because the rate card is priced per token and the decision is priced per task. Those two numbers come out of this matchup with opposite signs, and the reason is not verbosity.

What a finished task costs, not what a token costs

On Artificial Analysis Intelligence Index v4.3.2, the measured cost of completing one full index task is $0.21 for Claude Haiku 5.5 and $0.99 for Ling 3.1 Flash. That is a 4.6× gap — wider than the 3× input ratio, and in the same direction.

The obvious explanation — that the expensive model is talking more — is the wrong one. Ling 3.1 Flash generated 100,671 output tokens per index task; Claude Haiku 5.5 generated 162,164. The cheaper model is the chattier one, by more than 60%. So the money is not going where the rate card suggests either.

Split the per-task cost and it lands where the index methodology actually spends it. Of Claude Haiku 5.5's $0.21, roughly 62% is input-side; of Ling 3.1 Flash's $0.99, roughly 91% is. These are cache-heavy reasoning runs, and the two vendors price a cached input token very differently: $0.01 per million for Claude Haiku 5.5 against $0.06 per million for Ling 3.1 Flash — a 6× spread on the single line that dominates the bill. The published rate card compares $0.10 to $0.30. The line that decides the invoice compares $0.01 to $0.06.

Our own catalogue passes provider list prices through at 0% markup, which is how you can tell the two situations apart on a live bill rather than a modelled one. Ling 3.1 Flash is not on the catalogue at any spelling of its vendor path we could resolve — not under InclusionAI, not under Ling, not under any of the eight forms we tried — so the $0.30 and $0.90 above are the vendor's own published rates and nothing we can route for you today. Claude Haiku 5.5 is not on the catalogue either; it runs through Anthropic's own API. What the catalogue does carry from this comparison's neighbourhood is GLM 5.3 Flash, DeepSeek V4.1 Flash, Qwen3.8 Flash and Gemini 3.8 Flash, each at the provider's list price with 0% markup, so a vendor rate change reaches our side the same day it reaches theirs. If the finding below is that Ling 3.1 Flash's agentic profile is what your workload needs, the practical next step is a head-to-head against the agentic-capable models we do route, on one key with automatic failover underneath and the routing DSL when the cost ceiling belongs in the route rather than in application code.

Seven rows where both were measured

Artificial Analysis is the only board that has run both models, and the overlap is narrow but informative. The rows do not agree with each other, and the direction of disagreement is not random.

• Intelligence Index v4.3.2 — 43.40 for Claude Haiku 5.5 (Max) against 41.09 for Ling 3.1 Flash. A 2.31-point lead to Anthropic.

• Cost per finished Index task — $0.21 against $0.99 on the same board.

• Time per Index task — 424.6 seconds for Claude Haiku 5.5 against 360.4 seconds for Ling 3.1 Flash. This is the row where the expectation breaks: the 560-billion-parameter model is the faster of the two across the index as a whole, by about 15%.

• Terminal Bench 4.0 — 0.3283 against 0.3333. Ling 3.1 Flash is half a point ahead, which on a benchmark both vendors quote is a tie.

• SciCode — 0.5498 against 0.5405. Claude Haiku 5.5 by 0.9 points. Also a tie.

• Humanity's Last Exam, no tools — 0.4439 against 0.3943. Claude Haiku 5.5 by 5 points, the first real separation.

• AutomationBench, partial score — 0.3541 against 0.6174. Ling 3.1 Flash by 26 points, and by a wider margin than every other gap on this list combined.

Two more rows exist outside that shared set and are worth carrying, because they are the ones buyers notice most. On GDPval-AA, which Artificial Analysis runs across 44 occupations, the two are 1620.05 and 1621.75 — a statistical dead heat. On AA-Briefcase v1.1, an Elo-rated long-form professional-work evaluation, Claude Haiku 5.5 reads 1577.72 against Ling 3.1 Flash's 1400.35, a 177-point gap in Anthropic's favour. That is the widest non-agentic separation between them anywhere on the board.

The single row that should decide most of these conversations

Index-level averages hide where the time and money actually went, and this pair is the clearest case of that in the batch. The per-evaluation numbers on Terminal Bench 4.0 are not close in any dimension except the score.

• Terminal Bench 4.0, Ling 3.1 Flash — 0.3333 at $5.49 per task and 1,283 seconds per task.

• Terminal Bench 4.0, Claude Haiku 5.5 — 0.3283 at $0.65 per task and 908 seconds per task.

Five thousandths of a point of score separate them. The cost is 8.4× and the wall clock is 1.4×. A team that reads the benchmark table and picks the model scoring 0.3333 has, on Artificial Analysis's own accounting, chosen to pay eight times as much to arrive a third of a minute later with the same answer.

This is not an argument that the score is meaningless; it is an argument that a score is a ratio of work done to work attempted, and it says nothing about the resources consumed getting there. Benchmarks of agentic completion are exactly the setting where that distinction bites hardest, because an agent that retries is an agent that pays full price on every retry, and a model that costs eight times as much per attempt turns a retry loop into a budget line.

The 560 billion parameters with nothing to download

Here is the part of Ling 3.1 Flash's spec sheet that is genuinely unusual, and it is not the size.

Ling 3.1 Flash is a sparse mixture-of-experts model — 560 billion parameters total, 25 billion active per token — and Artificial Analysis classifies it as proprietary. There are no weights, no licence file and no repository. A large sparse model of that shape would normally arrive as an open release, because that is what the rest of this tier does: GLM 5.3 Flash ships MIT weights at 320 billion total and 18 billion active, and DeepSeek V4.1 Flash ships MIT weights at 552 billion total and 16 billion active. Ling 3.1 Flash is the same class of architecture at the same order of scale, published as an API only.

That choice has a consequence that the benchmark rows do not show. A 25-billion-active model has to be served somewhere, and if you cannot hold the checkpoint, you cannot choose where or at what margin — you rent the vendor's serving of it. The $5.49 Terminal Bench task price above is not primarily a token-rate artefact; it is what it costs to rent a large sparse model from the only party that can run it. The open-weights siblings in this tier give you the option to move that number yourself.

It cuts the other way too, and the same honesty applies. An open release is not automatically the better product: you inherit the serving burden, the quantisation choices and the upgrade treadmill along with the weights, and a licence file is not a support contract. Anthropic publishes a retirement commitment for Claude Haiku 5.5 of not sooner than October 7, 2027, on a first-party API with a system card. Which of those two arrangements you want is a question about your team, not about either model.

What Ling 3.1 Flash is for

Read the profile whole and it describes a coherent product rather than a compromised one: a large, sparse, long-context model that finishes autonomous multi-step workflows better than anything else measured in this price band, is unremarkable at hard single-shot reasoning, and does it at a flat rate with no prompt-length cliff. On AutomationBench it is 26 points clear of Claude Haiku 5.5, and its AA-Briefcase score is the only point in this comparison where it lands behind the middle of the field rather than the front of it.

That is a defensible description of a batch agentic worker: point it at a queue of structured jobs that must each be completed, accept that the task is measured in minutes, keep the prompt long enough that a threshold step would be punitive, and value reliability at the finish line above the cost of any single token. What it is not good for is the thing flash-tier models are usually bought for. It accepts text only — no image input at all, where Claude Haiku 5.5 takes text and images. Its index task time is shorter, but its Terminal Bench task time is longer, and at $0.99 per finished index task against $0.21 it is spending four and a half times as much to land on nearly the same composite.

Which of the two, on the evidence that exists

If your work is text in, text out, priced per token at volume, Claude Haiku 5.5 wins this comparison on every line that reaches an invoice: lower index task cost, lower real task cost on the benchmark that matters most for agents, higher composite, better long-form professional-work scores, better fidelity, and image input that the other model does not offer at all.

If your work is an unattended agent that has to complete a multi-step workflow, Ling 3.1 Flash is scoring 26 points above Claude Haiku 5.5 on the one shared benchmark that measures exactly that, and that is worth testing rather than dismissing on price. Test it on your own trace, not on this table — and price the test per completed job, because that is the number that moves when an agent retries.

If you need to hold the weights, neither of these two is your model. Ling 3.1 Flash is proprietary at 560 billion parameters; Claude Haiku 5.5 is proprietary with no published count. The open releases in this tier are elsewhere on the board, and that comparison starts from a different set of rows entirely.

The composite says 2.31 points. The agentic benchmark says 26 the other way. The rate card says 3× on input; the finished-task cost says 4.6× on the same direction as the card. Four numbers, two directions — which is why the only reliable way to settle this is to run both against a trace of your own work and read the cost off the completed jobs rather than the tokens.