LLM Coding Cost Benchmark: 13 Models, Tested

Everyone quotes per-token prices; almost no one runs the code and tells you what a model actually costs to get a coding task right. So we did: 13 models, the same nine executed coding tasks, every answer run against hidden tests, with real billed cost, latency and reasoning tokens recorded. The headline in 2026 is not who is smartest — 10 of the 13 scored a perfect 9/9 — it is how differently they charge for it. The same 9/9 costs $0.10 per 1,000 tasks on the cheapest model and $8.83 on the priciest: an 88x spread for identical output.

What we tested

We gave 13 models the same nine coding tasks, executed every answer against hidden test cases, and recorded the real billed cost, latency, and reasoning tokens for each call. This is a fresh, single-harness run in July 2026 — every model measured the same way, so the numbers are directly comparable. The line-up spans the closed frontier, the open-weights field, and the cheap coding specialists:

• Closed frontier: GPT-5.5 (OpenAI), Claude Opus 4.8 and Claude Sonnet 5 (Anthropic), Grok 4.3 (xAI)
• Open-weights flagships: GLM 5.2 (Z.ai), Nemotron 3 Ultra (Nvidia), Mistral Medium 3.5, MiniMax M3, Kimi K2.7-Code (Moonshot), StepFun 3.7 Flash
• Cheap coding specialists: Qwen3 Coder Next (Alibaba), DeepSeek V4-Flash and V4-Pro

The nine tasks mix classic interview problems with edge-case-heavy parsing and stateful logic, so a model has to handle detail, not just recognise a pattern: two-sum (hash-map pairing), valid-parentheses (stack matching), merge-intervals (sort then merge), Roman-to-integer (subtractive notation), longest-common-subsequence (DP), nested-dict flatten (path-joined keys), top-k words (frequency with an alphabetical tiebreak), a token-bucket rate-limiter (stateful timing), and a CSV-line parser (quoted fields with escaped double-quotes). Each is given as a signature plus spec — no example tests — and graded only by running the code.

Results: almost everyone passed

Ten of thirteen solved all nine. That is the real 2026 story: on bounded, well-specified coding tasks the frontier has converged, and a perfect score no longer separates a $8.83 model from a $0.10 one. The three misses were single slips, each on a different task:

There was no single task that broke everyone — the misses spread across three unrelated problems — which reads more like a one-off slip than a capability wall. Notably, the two priciest models (GPT-5.5, Claude Opus 4.8) and the two cheapest (Qwen3 Coder Next, DeepSeek V4-Flash) all landed on the same perfect 9/9.

Results: the 88x cost gap

Here is where the models stop looking alike. Same nine tasks, same score for most of them — wildly different bills. This is the real billed cost to run all nine, scaled to 1,000 tasks:

Speed: the fastest was not the priciest

The uncomfortable finding: neither price nor size predicted correctness — and the fastest model was a mid-priced one. GPT-5.5 ($8.83/1k) and Qwen3 Coder Next ($0.10/1k) both scored a perfect 9/9, so GPT-5.5 cost 88x more for the identical result. Claude Opus 4.8 cost 40x what Qwen did for the same score. If your coding work looks like these tasks — bounded, well-specified, one-shot — paying frontier rates by default is leaving money on the table.

Speed told a parallel story. The quickest model was not the most expensive: Mistral Medium 3.5 answered in 2.9 seconds a task and still went 9/9, more than twice as fast as GPT-5.5 (10.5s) and Claude Opus 4.8 (6.1s). The reason is architectural: Mistral, Qwen and the Claude models are effectively non-reasoning here — they emit no visible chain-of-thought — so they return an answer instead of deliberating first. That does not make Opus or GPT-5.5 bad models; it makes them misallocated for routine work. Nine one-shot tasks do not exercise where a frontier model earns its price: large multi-file refactors, ambiguous specs, long agentic chains.

Reasoning overhead

Cost and latency both trace back to one variable: tokens generated. Because per-task cost = price × tokens, a reasoning-heavy model can turn a cheap sticker price into an expensive bill.

• Zero-reasoning, quick and cheap: Qwen3 Coder Next, Mistral Medium 3.5 and the Claude models emitted no visible reasoning tokens — they were among the fastest and cheapest per task.
• Heavy reasoners paid for it: DeepSeek V4-Pro (~732 reasoning tokens/task, 18.2s) and MiniMax M3 (~623, 13.4s) spent the most tokens — and V4-Pro was one of the three that still dropped a task, so the extra deliberation did not buy correctness here.
• Cheap but not fast: DeepSeek V4-Flash is the value story on cost ($0.13/1k) but reasons before answering (14.5s), so it wins on dollars, not on latency.

The lesson repeats the cost one: reasoning is a feature you want on hard problems and an expense you don't want on easy ones — another argument for routing by difficulty rather than defaulting every request to a heavy model.

What this proves (and doesn't)

• It proves: on standard, well-specified coding tasks, quality has converged — 10 of 13 models, cheap and frontier alike, hit a perfect score — so the durable differentiators are real cost (an 88x range) and latency, not pass rate.
• It does not prove: capability on hard, ambiguous, or repo-scale agentic work. These are nine bounded problems, one run each; standard problems can appear in training data.
• So treat it as: a cost-efficiency reality check that should make you test cheap models on your own tasks before paying frontier rates — not a final capability ranking. Method and caveats: how we test.

How to act on it

The practical play is cheap-first routing with escalation: send routine coding to Qwen3 Coder Next or DeepSeek V4-Flash, keep a fast mid model like Mistral Medium 3.5 for latency-sensitive calls, and escalate only the genuinely hard tasks to Claude Opus 4.8 or GPT-5.5. Done well, you keep most of the 88x savings while still having a flagship on tap.

The catch is integration: 13 models normally means 13 SDKs, keys, and dashboards. An OpenAI-compatible gateway collapses that to one — same endpoint, swap the model id, one bill — which is what makes cheap-first routing practical instead of aspirational.

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