Local LLMs vs GPT-4 and Claude: Benchmark Results (2026)

Dated snapshot: This post reflects the local-vs-cloud landscape as it stood in February 2026. Both the cloud lineup (GPT-4o and Claude 3.5 Sonnet are no longer the current flagships) and the local field have moved on since. Treat the figures below as a historical snapshot, not current standings. For a maintained, sourced comparison, see our local vs cloud benchmark leaderboard.

TL;DR: By February 2026 the strongest open-weight models had already caught the cloud flagships of the previous generation on knowledge benchmarks. DeepSeek publishes 90.8 MMLU (Pass@1) for the open-weight R1 against 88.3 for Claude 3.5 Sonnet and 87.2 for GPT-4o in the same table (DeepSeek-R1 model card). Very long context remained the cloud's clearest edge.

MMLU (Pass@1) exactly as DeepSeek published it in the R1 model card: the open-weight R1 sits above every cloud model in that table except OpenAI o1-1217.

Where Do Local Models Stand? (February 2026)

Every score below is quoted verbatim from the vendor's own model card, with the benchmark named. Read down the column, not across it: MMLU, MMLU-Redux and MMLU-Pro are different tests run by different evaluators, so a 94.0 MMLU-Redux is not "better" than an 88.3 MMLU. For an apples-to-apples ranking on one benchmark, use our maintained leaderboard.

Sources: Claude 3.5 Sonnet, GPT-4o and DeepSeek-R1 MMLU from the DeepSeek-R1 model card. Llama figures from Meta's Llama 3.1 model card (instruction-tuned, MMLU 5-shot). Qwen figures from the Qwen3.5-27B and Qwen3.5-9B model cards. Rows marked "Not vendor-published" had no raw primary source we could verify, so no number is printed rather than a guess. Speed figures are estimates for Apple Silicon, not measurements; ModelFit runs no benchmarks of its own. Parameter counts for closed API models are undisclosed by their vendors.

How Do the Tiers Break Down?

Tier 1: Frontier open weights (DeepSeek-R1, Qwen3.5-122B-A10B, Llama 3.1 405B)

These sit level with the cloud flagships of the preceding generation on knowledge tests, and they need a workstation to run. DeepSeek's own table puts R1 at 90.8 MMLU against 88.3 for Claude 3.5 Sonnet and 87.2 for GPT-4o. Meta puts Llama 3.1 405B Instruct at 87.3 MMLU (5-shot).

Verdict: on published scores the frontier open-weight models had caught up. What you paid instead was the machine.

Tier 2: High-performance, workstation class (Llama 70B, Qwen3.5-27B and 35B-A3B)

This is where most serious local work happened. Meta reports 83.6 MMLU (5-shot) for Llama 3.1 70B Instruct. Qwen reports 93.2 MMLU-Redux for Qwen3.5-27B and 93.3 for the 35B-A3B mixture-of-experts, which need 24 GB and 32 GB of RAM respectively at Q4.

Verdict: a 27B model on a 32GB Mac was the sweet spot for quality per gigabyte, and the MoE variant kept the speed of a much smaller model.

Tier 3: Consumer, laptop class (4B to 9B models)

Qwen reports 91.1 MMLU-Redux for Qwen3.5-9B and 88.8 for Qwen3.5-4B, which is the tier that fits a 16GB laptop with room for context. Meta reports 69.4 MMLU (5-shot) for Llama 3.1 8B Instruct.

Verdict: good enough for chat, summarising and everyday coding help, and the only tier most people ever need.

Projection: When Do Locals Catch Up to GPT-4?

Gap History

⚠️ The table below is a contemporaneous narrative estimate, not measured data. The percentage gaps were the author's read of the field in February 2026, compiled across model generations that were never evaluated head to head. They are kept as a record of how the trend looked at the time. Every measured figure in this post is in the sourced tables above and below.

Forecast (Speculative, February 2026, now superseded)

⚠️ This forecast has not held up. It projected milestones against a February 2026 cloud lineup (GPT-4, GPT-4o, Claude 3.5) that the industry has since moved past on both the cloud and local sides. It is kept here only as a historical record of what was projected at the time, not as a current outlook.

Based on the trend as it looked in February 2026:

Caveat (original, February 2026): Clouds evolve too (GPT-5, Claude 4...). The race is permanent. For where local models actually stand today, see the maintained local vs cloud benchmark leaderboard.

What Do the Detailed Benchmarks Show?

Coding: SWE-bench Verified

SWE-bench Verified asks a model to fix real GitHub issues, which is the closest public proxy for day-to-day coding work. DeepSeek published these in one table, so the rows are directly comparable to each other.

Source: DeepSeek-R1 model card.

Qwen publishes SWE-bench Verified for its own 2026 models in a separate table, evaluated separately, so treat this as a second reading rather than an extension of the one above: 72.4 for Qwen3.5-27B, 72.0 for Qwen3.5-122B-A10B and 69.2 for Qwen3.5-35B-A3B, against 72.0 for GPT-5-mini (Qwen3.5-27B model card).

On the older HumanEval pass@1 test, Meta reports 89.0 for Llama 3.1 405B Instruct, 80.5 for the 70B and 72.6 for the 8B (Llama 3.1 model card).

Local leader: DeepSeek-R1 landed within two points of Claude 3.5 Sonnet on SWE-bench Verified, with weights you can download.

Reasoning and math

Source: DeepSeek-R1 model card. R1 and o1 spend test-time compute (long chains of thought), which is most of why they clear the non-reasoning models by so much here.

Context Length

Cloud advantage: very long context was still dominated by Gemini. Qwen's own serving instructions cap the context at 262,144 tokens (Qwen3.5-27B model card).

What Should You Run Today?

For the Average User (MacBook 16-24GB)

Today:

• 16GB: Qwen3.5-9B, which Qwen scores at 91.1 MMLU-Redux
• 24GB: Qwen3.5-27B, at 93.2 MMLU-Redux and 72.4 on SWE-bench Verified
• Acceptable speed for interactive use (an estimated 52 tok/s on the 27B)
• 100% offline, $0 API

In 6 months (July 2026, as projected then):

• GPT-4 equivalent on a standard MacBook
• 50-70B models with optimized MoE architecture

For the Power User (Mac Studio 128GB)

Today:

• DeepSeek-R1 lands within two points of Claude 3.5 Sonnet on SWE-bench Verified (49.2 against 50.8)
• Llama 3.1 405B or Qwen3.5-122B-A10B for the highest published knowledge scores you can self-host
• OK speed (an estimated 12 to 35 tok/s)
• Cost: Mac Studio ~$4K once vs API for life

ROI: Break-even in ~6-12 months vs intensive API use. Related: See our DeepSeek-V3 vs Qwen 3.5 head-to-head, the Qwen 3.5 Small models analysis, or find the best model for your MacBook.

Frequently Asked Questions

Have local LLMs caught up to GPT-4?

On knowledge benchmarks, yes. DeepSeek's own evaluation table puts its open-weight R1 at 90.8 MMLU against 87.2 for GPT-4o and 88.3 for Claude 3.5 Sonnet. On MMLU-Redux the same table reads 92.9 for R1 against 88.0 for GPT-4o. The remaining gaps are very long context and the hardware bill, not raw quality.

What is the best local LLM for a MacBook with 16GB RAM?

Qwen3.5-9B is the strongest pick that leaves room for context on a 16GB Mac. Qwen reports 91.1 MMLU-Redux for it, above the 88.0 MMLU-Redux that DeepSeek's table records for GPT-4o, though the two figures come from different evaluators and are not a controlled comparison. See our MacBook Air and MacBook Pro pages for device-specific recommendations.

How much does it cost to run local LLMs vs cloud APIs?

Local models are free after hardware purchase. A Mac Studio M3 Ultra ($4,000, 96GB) running Qwen3.5-122B-A10B breaks even vs intensive API use ($50-100/month) in 6-12 months. A MacBook Pro 24GB ($1,900) with Qwen3.5-35B-A3B breaks even even faster.

Which local model is best for coding?

Among the models in DeepSeek's table, R1 leads the open-weight field at 49.2 on SWE-bench Verified, two points behind Claude 3.5 Sonnet, but it needs a workstation. On a machine you can actually carry, Qwen reports 72.4 on SWE-bench Verified for Qwen3.5-27B and 69.2 for the 35B-A3B mixture-of-experts, evaluated on Qwen's own harness rather than DeepSeek's.

Will local models surpass cloud models?

This was the open question as of February 2026: based on the trend at the time, local models were projected to reach full GPT-4o parity by mid-2026 and potentially surpass cloud flagships by mid-2027. That projection has since been overtaken by newer cloud and local generations; see the maintained local vs cloud benchmark leaderboard for where things actually stand now.

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Last updated: February 24, 2026 Sources: every benchmark score above is quoted verbatim from a vendor's raw model card: the DeepSeek-R1 model card, Meta's Llama 3.1 model card, and the Qwen3.5-27B and Qwen3.5-9B model cards. ModelFit does not run its own benchmarks. Scores from different cards were produced by different evaluators and are not controlled comparisons. Tokens per second are estimates, not measurements. Related Model Families:

• Qwen Models: Widest size range, strong multilingual and coding
• Llama Models: Most popular open-weight family
• DeepSeek Models: Best-in-class reasoning with R1

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