Claude Haiku 5.5 vs Gemma 4 12B: A Weights-You-Can-Hold Model Against a Vendor API

Claude Haiku 5.5 and G​emma 4 12B are not really competing for the same slot, and the fastest way to see that is a single line: one of them ships as Apache-2.0 weights you can download, quantise and run on your own hardware, and the other exists only behind an API. Claude Haiku 5.5 arrived on October 7, 2026 and immediately redrew what a small tier can score — 43 on the independent Artificial Analysis Intelligence Index. G​emma 4 12B sits at 14 on the same board. That gap is enormous, and it is not the reason most teams end up choosing between them.

The choice is usually forced before any benchmark is consulted: a regulated pipeline that cannot send tokens to a vendor, an edge box with no reliable egress, a latency budget measured in milliseconds, or a fine-tuning requirement that a closed API will never satisfy. If none of those applies to you, the answer is not close. If one does, the score gap stops mattering and the deployment constraint decides the whole thing.

What the board measured, and when

The independent figures below come from Artificial Analysis, and the vendor rates come from A​nthropic's and G​oogle's own documentation. They are two different kinds of number and are labelled as such throughout.

• Independent score — Claude Haiku 5.5 at 43 on the Intelligence Index, second of 182 models. G​emma 4 12B (Reasoning) at 14, 21st of 142 in its class. A 29-point gap.

• Input price per million tokens — Claude Haiku 5.5 $0.10 up to 100,000 tokens and $0.50 above; G​emma 4 12B $0.10.

• Output price per million tokens — Claude Haiku 5.5 $0.50 up to 100,000 tokens and $2.50 above; G​emma 4 12B $0.30.

• Context window — 1,000,000 tokens for Claude Haiku 5.5; 262,000 for G​emma 4 12B.

• Throughput — 240.4 output tokens per second for Claude Haiku 5.5; 113.1 for G​emma 4 12B, with a 2.48-second time to first token.

• Cost per finished Index task — $0.21 for Claude Haiku 5.5. G​emma 4 12B is cheap enough per token that the board's blended figure lands at $0.12 per million tokens, but derived per-task cost is not the number that should decide this comparison.

• Weights — Apache 2.0 for G​emma 4 12B, downloadable, roughly 12 billion parameters dense. Claude Haiku 5.5 is proprietary; there is no weights release and none is planned.

• Age — G​emma 4 12B has been out since June 2026. Claude Haiku 5.5 is two days old at this writing.

The gap is 29 points, and the price gap is almost nothing

Look at the two price rows again: $0.10 in for both, and $0.30 against $0.50 on output. G​emma 4 12B is cheaper, and the difference is small enough that it will be swamped by token counts — Claude Haiku 5.5's newer tokenizer means the same text becomes roughly 30% more tokens, so a raw output-rate advantage of 40% on G​emma's side is closer to a wash on a real bill.

So you are paying nearly the same rate for a model 29 Index points behind, or you are paying nearly the same rate for a model 29 points ahead, depending on which column you read first. That is the honest framing and it should be stated plainly: on any task both models can do, Claude Haiku 5.5 is the better buy at list price, and it is better by a margin that no amount of prompt engineering on the smaller model will close.

The interesting question is therefore not "which is better". It is "which of the tasks in my queue does G​emma 4 12B fail", and the answer to that is the only thing that decides the comparison.

What weights buy you that an API cannot

A downloaded model is a different kind of asset, and the advantages are structural rather than incremental.

• Data boundary — with G​emma 4 12B there is no vendor in the loop at all. For health, legal, defence or financial workloads, that is frequently the only requirement that matters, and no amount of score makes an API acceptable.

• Fixed cost instead of variable — a 12B dense model quantised to four bits fits comfortably on a single modern accelerator. At that point the marginal cost per token is electricity, and a workload can be sized against a machine rather than a meter.

• No egress, no network latency — an on-premises 12B model answers in milliseconds because nothing leaves the box. A vendor API pays a round trip and a cold-start tail that an edge deployment simply does not have.

• Fine-tuning and distillation — you can adapt G​emma 4 12B on your own data, ship the adapter, and freeze it. Whether A​nthropic will ever let you fine-tune a Haiku-tier model is not something you can plan a roadmap around.

• A floor under your roadmap — weights cannot be deprecated out from under you. A​nthropic has committed to not retiring Claude Haiku 5.5 before October 7, 2027, which is generous, but a commitment with a date on it is still a commitment with a date on it.

• Modality, oddly — the board records G​emma 4 12B taking text, image, speech and video input for text output, which is a wider intake than Claude Haiku 5.5's text-and-image. For a local pipeline that ingests audio without a separate transcription service, that is a real capability the API side does not have.

Where the 12B does not survive contact

The same board that measures the 29-point gap also records the things a small dense model cannot do well, and skipping them would be dishonest:

• Long context — 262,000 tokens against 1,000,000. A pipeline that stuffs a codebase or a year of records into one prompt has no path on G​emma 4 12B, and chunking it into a retrieval loop adds engineering that the larger window would have avoided.

• Agentic and computer-use work — the class of task where a small model's failure is silent and expensive. A​nthropic's own vendor-reported figures for Claude Haiku 5.5 put OSWorld 2.1 at 72.4% against 15.7% for the previous Haiku; a 12B model with an Index of 14 is not the tool for that job, and the vendor numbers here are a useful signal even allowing for the fact that no independent run has reproduced them.

• Throughput per dollar on a shared fleet — 113 tokens per second on a hosted instance is fine, but the reason to self-host is not speed, and a single-GPU deployment will be slower still.

• Nobody's fallback — if you run G​emma 4 12B in production, an outage of your own hardware is your outage, with no second provider to fail over to unless you build one.

Running either of them through one endpoint

OrcaRouter carries Gemma 4 31B and Gemma 4 26B-A4B on the catalogue today at Google's list price with 0% markup, but not the 12B — and it is worth saying that plainly rather than implying otherwise. The 12B is reachable through the vendor's own distribution and several third-party platforms. Claude Haiku 5.5 is likewise not on the catalogue yet; it is available from A​nthropic's API and the major clouds.

What a router does offer is the shape this comparison actually calls for. A sensible deployment of a cheap local model and an expensive API model is not a fork — it is a route: G​emma 4 12B or a hosted G​emma 4 variant answers the easy majority, and requests that trip a quality or length condition escalate to an A​nthropic leg. Claude Haiku 4.5, Claude Sonnet 5.5 and Claude Opus 5.5 are on the catalogue now, so the escalation path can be built and load-tested before the small-tier leg is even available. On OrcaRouter that composition is a routing DSL expression and automatic failover rather than a service you maintain, and with provider list prices passed through at 0% markup, a price move on any leg is live the same day it happens.

The rule

Pick Claude Haiku 5.5 unless a constraint rules it out. It is 29 Index points ahead of G​emma 4 12B for close to the same list price, it takes a million tokens of context, and it is the stronger model on every task both can attempt. There is no version of this comparison where the 12B wins on merit.

Pick G​emma 4 12B when the constraint is the point — when the tokens cannot leave your premises, when the budget is a machine instead of a meter, when you need to fine-tune, or when you need weights in hand rather than a rate card and a retirement date. Those are not preferences, they are requirements, and against a requirement a 29-point gap is simply not the deciding number.