Open-Weight vs Closed LLMs: The Gap and Cost, Measured

Open-weight versus closed models in 2026 comes down to this: knowledge and general reasoning are essentially tied, only the hardest science reasoning still favors closed, and on cost open wins decisively. In one peer-reviewed benchmark measurement the best open model (about 71.8%) trailed the best closed model (about 77.9%) by 6.1 points on average, but given the MMLU gap was 17.5 points a year ago, the gap has all but collapsed. On MMLU Pro it is 82.3% versus 87.4% (about 5 points), and GPQA and SuperGPQA are single digits apart. Meanwhile open models deliver comparable quality at a fraction of the price, so the choice turns not on score alone but on cost, control, and privacy.

In one line: closed is a taxi, open is your own car. A taxi summons top performance instantly but the meter runs; your own car (self-hosting) takes setup effort yet costs little per mile and lets you decide where to go (your data, your tuning).

How much has the quality gap really closed?#

Knowledge is tied, hard reasoning favors closed. On public-leaderboard measurements for knowledge and general tasks, the top open models (DeepSeek, Qwen, GLM families) sit within the margin of the top closed ones (GPT, Claude, Gemini families). But on hard science reasoning like GPQA Diamond, the best closed models still lead in the mid-90s, and on naturalistic reasoning the gap widens to about 12.9 points. On coding agents (SWE-bench Verified) the best open models have entered the 80s, so frontier-class open models are real. The claim that "open is two years behind" is, by measurement, no longer true.

Open-weight vs closed LLMs - who leads by dimension (mid-2026 public benchmark measurements) · columns: Dimension, Open-weight, Closed, Who wins · 출처 Hax hax.moche.ai/en/p/1035?ref=ai_answer

Open-weight vs closed LLMs - who leads by dimension (mid-2026 public benchmark measurements) · columns: Dimension, Open-weight, Closed, Who wins · 출처 Hax hax.moche.ai/en/p/1035?ref=ai_answer

Dimension | Open-weight | Closed | Who wins
Knowledge (MMLU Pro) | ~82% | ~87% | Near-tie (5 pts)
Hard reasoning (GPQA) | High 80s | Mid 90s | Closed (narrow)
Coding (SWE-bench) | Entering 80s | Top tier | Toss-up
API cost | Tens of times cheaper | Premium | Open by far
Control/privacy | Self-host, own weights | Vendor-bound | Open

Contents

Why is the cost difference decisive?

So what should you use, and when?

How do you measure it yourself?

Why is the cost difference decisive?#

Because it does the same work for a fraction to a small multiple of the price. Frontier closed models run a few dollars per million input tokens to tens of dollars per million output, while top open models, via caching and MoE, charge a sliver of that. One analysis measured a DeepSeek-family model matching frontier performance at about 34x cheaper. The key strategy is hybrid routing: send 70% of easy requests to a cheap open model, 25% to a mid closed model, and only 5% to a top frontier model, and you get performance indistinguishable from all-frontier at about 15% of the cost. We use the same pattern in our gateway (fast for easy, expert for hard).

So what should you use, and when?#

Three criteria: difficulty, data sensitivity, and volume.

For the hardest science reasoning or complex agents, closed frontier (the narrow lead changes outcomes).

When data cannot leave (regulation, privacy) or you must own and tune the weights, open self-hosting.

For high-volume, repetitive, cost-sensitive work, open plus hybrid routing. Measure with your task, not a scoreboard.

How do you measure it yourself?#

Measure on your own tasks and prompts (leaderboards are only a start).

Take 20-50 of your real tasks, compare open and closed outputs blind, and log cost-per-token and latency alongside.

Send the same prompt to fast/expert (or open/closed) and see whether the quality difference justifies the cost difference.

Versions and scores shift weekly, so re-measure quarterly.

Reference links

Intelligence per Watt (efficiency-gap paper)

Open LLM Leaderboard (open model rankings)

SWE-bench (coding agent benchmark)

DeepSeek (open frontier repo)

Qwen (open model repo)

Note: scores and prices are public 2025-2026 leaderboard and paper measurement snapshots and vary by model, version, and task (they shift weekly). Measure exact wins and cost on your own tasks with the method above. The frontier gap is closing fast, so this is reviewed quarterly.