MiniMax M3 vs GPT-5.5: SWE-Bench Pro, 8× Price Gap, A/B Both via ofox (2026)

TL;DR — MiniMax shipped M3 on June 1, 2026 as the first open-weight model to credibly land on the SWE-Bench Pro leaderboard, scoring 59.0% versus GPT-5.5’s 58.6% on the same benchmark. Headline numbers are within margin-of-error, but the price columns are not. M3 lists at $0.60 input / $2.40 output per million tokens; GPT-5.5 sits at $5 / $30. Blended that is roughly 8–12× cheaper for the same SWE-Bench Pro point. GPT-5.5 still owns Terminal-Bench (82.7% vs M3’s 66.0%) and the Codex CLI ecosystem, so the right answer depends on whether your workload looks more like refactor or more like shell. Both models are on ofox.ai under the OpenAI-compatible endpoint, so the comparison is a one-line model swap — not a migration.

MiniMax M3 is the first open-weight model to clear 59% on SWE-Bench Pro — at $0.60 / $2.40 per million tokens, the cost per SWE-Bench Pro point runs roughly 11× lower than GPT-5.5.

TL;DR: Which One Should You Pick?

The 30-second answer, before the rest of the article:

The honest verdict. For most teams running coding agents at scale in 2026, MiniMax M3 is the new default for the cost-sensitive half of the workload, GPT-5.5 stays on the latency-sensitive half, and the genuinely hard tasks route to Claude. The two-model split below covers the realistic 80% of your traffic.

What Each Model Actually Shipped

Both releases happened within six weeks of each other. The framing matters before the numbers.

GPT-5.5 launched on April 23, 2026 as OpenAI’s single coding flagship — three variants (standard, Thinking, Pro) on the same model weights, differentiated by reasoning budget. The launch headline was agentic coding: Terminal-Bench at 82.7%, SWE-Bench Verified at 88.7%, a 60% drop in hallucinations versus GPT-5.4, and a doubled price tag (from $2.50/$15 to $5/$30 per million tokens). Codex CLI was the showcase surface — the 82.7% Terminal-Bench number runs through Codex, not through a vanilla harness, which matters when you read the comparison numbers below.

MiniMax M3 dropped on June 1, 2026 as MiniMax’s first frontier-class release. The headline was a different kind: same 1M context as GPT-5.5, but with a new MiniMax Sparse Attention (MSA) architecture that delivers more than 9× faster prefill and more than 15× faster decoding than the previous M2 generation at full 1M context, at one-twentieth the per-token compute. Pricing came in at $0.60 / $2.40 per million tokens — 5–10% of GPT-5.5’s rate card — and the model was native multimodal (image and video understanding) out of the box. Open-weight release on Hugging Face was promised within ten days of launch, putting M3 at the same SWE-Bench Pro tier as a closed frontier model while being runnable on commodity GPUs.

The headline isn’t that M3 is faster than GPT-5.5 in every test. It’s that the cost-per-point math now decisively favors open-weight on a benchmark that used to be closed-source territory.

Quick Specs Comparison

The boring numbers, side by side. Use this as the reference card; the deep analysis follows.

Two things to flag from the spec sheet. First, the context window is identical — both are 1M, both run real workloads in that window. M3’s MSA architecture is faster on long contexts but the ceiling is the same number. Second, the open-weight column is the silent kingmaker — if your compliance, IP, or air-gap story rules out sending source code to a third-party API, M3 is the only option at this benchmark tier.

Coding Benchmark: Real Tasks, Not Just SWE-Bench

The SWE-Bench Pro number gets the headlines, but the benchmark portfolio matters more for a real routing decision. Here is the published picture from both vendors plus the third-party data available as of mid-June 2026.

Three reads on that table.

SWE-Bench Pro is a statistical tie, not a clean win. A 0.4-point gap on a benchmark with hundreds of tasks sits inside the typical re-run variance. Both vendors published their own numbers; independent rerun data from Artificial Analysis and LMArena had not yet landed for M3 as of June 15. Treat M3’s 59.0% as “approximately tied with GPT-5.5” until the third-party harness numbers arrive — that is the genuinely honest framing. The price gap is the unambiguous part.

Terminal-Bench is GPT-5.5’s home court. The 16.7-point gap on Terminal-Bench 2.1 is too large to attribute to noise, and the asterisk matters: OpenAI runs Terminal-Bench through Codex CLI, which is purpose-built for terminal agentic loops. M3’s number is the model in a more generic harness. If your team ships work through Codex CLI, switching the underlying model from openai/gpt-5.5 to minimax/minimax-m3 is not a free move — you are giving up integration depth, not just a benchmark point. We unpack the Codex configuration story in detail in the Codex CLI multi-provider guide.

The benchmarks each vendor chose to publish reveal the positioning. MiniMax leaned into MCP Atlas and BrowseComp — agentic browser and tool-use benchmarks where 1M context and multimodal input pay off. OpenAI leaned into GPQA Diamond, MMLU, and Terminal-Bench — pure reasoning and shell agentic. The lack of overlap means a head-to-head on all eight is impossible from published numbers alone; on the four where both published, the score is one win each on the two coding-adjacent benchmarks (SWE-Bench Pro to M3 by a hair, Terminal-Bench to GPT-5.5 by a clear margin).

Pricing Math: Real Monthly Bill on a Realistic Workload

Sticker pricing is straightforward. The interesting number is what your invoice looks like at scale.

Assume a coding-agent workload of 30 million tokens per day split 2:1 input to output (20M in, 10M out). That is the rough shape of a 10-engineer team running Claude Code, Cursor, or a homegrown agent loop full time. Here is the monthly math for each model:

Same workload, one model is $1,080 per month and the other is $12,000. The 11.1× ratio holds across realistic input/output mixes — if your output ratio shifts higher (longer code generation), the gap widens; if it shifts lower (more code reading than writing), the gap narrows but stays above 8×.

Cost per SWE-Bench Pro point gives the cleaner one-line comparison:

GPT-5.5 costs roughly 11× more per SWE-Bench Pro point than MiniMax M3. That is the number to put on a slide if you are pitching the switch internally. It does not mean GPT-5.5 is wrong to use; it means the burden of justification on staying with GPT-5.5 has shifted to “what specifically does the 11× premium buy me on this workload?” — and that answer is real but narrower than it was a month ago. The full case for ofox-side cost optimization across the model stack is in our $30 AI coding stack guide.

When to Pick MiniMax M3

Four scenarios where M3 is the obviously correct call:

1. Batch / async coding agents — overnight code review, dependency upgrades, refactor sweeps, doc generation. These run as background jobs where latency and per-call interactivity don’t matter; total token spend dominates. M3 lands the same SWE-Bench Pro tier at one-eleventh the cost.
2. Long-context summarization and codebase RAG — anything past 200K input tokens, M3’s MSA architecture pays a real speed dividend over standard attention. The 15× decoding speedup at 1M context is reproducible in published benchmarks; it shows up as faster wall-clock time on long-context jobs.
3. Multimodal code review — diff screenshots, terminal session recordings, UI mockups passed alongside code. M3 handles both images and video natively in one call; GPT-5.5 supports image input but has no video understanding, which forces frame-by-frame stitching logic or a separate model call for any recorded-session use case.
4. Air-gapped or compliance-sensitive deployment — open weights on Hugging Face mean you can run M3 on your own infrastructure with no third-party API in the loop. GPT-5.5 has no on-prem path. If your compliance team has any opinion on source code traversal, M3 is the only frontier-tier model that even enters the conversation.

The fifth scenario — sometimes — is cost ceiling tripped. If you have run GPT-5.5 in production for a month and your invoice came back with a number that surprised your CFO, M3 buys you breathing room to keep the agent program funded.

When to Pick GPT-5.5

Four scenarios where the premium is honestly worth it:

1. Codex CLI is your primary surface — OpenAI’s terminal agent loop is materially better-tuned against GPT-5.5 than against any other model. Terminal-Bench 2.1 at 82.7% is a real ceiling, and the integration depth (file handles, shell history, multi-turn recovery from failed commands) is not something a model swap inherits. The Codex CLI configuration guide covers the trade-offs in detail.
2. Latency-sensitive interactive coding — pair-programming flows, autocomplete-style code generation, IDE integrations where every additional second of latency hurts adoption. GPT-5.5’s standard variant has been tuned for short prompts and fast first-token. M3 at 1M context is fast for long contexts, but on a 5K-token interactive prompt GPT-5.5 still wins on first-token latency.
3. Reasoning-heavy non-coding work mixed into the workload — GPQA Diamond 93.6% and MMLU 92.4% reflect a model trained against a broader reasoning corpus. If your coding agent is also occasionally asked to write a research summary, debug an architecture diagram, or produce a postmortem, GPT-5.5’s general-reasoning ceiling is higher.
4. You need vendor support for a managed deployment — OpenAI Enterprise contracts, ChatGPT Enterprise integrations, SOC 2 / HIPAA workflows already in place — switching to a Chinese vendor for the core coding model is a procurement story that often costs more than the API savings. GPT-5.5 may be the right answer for “the model my legal department already approved.”

When NOT to Pick Either (and What to Use Instead)

Neither M3 nor GPT-5.5 is the right answer for the hardest tasks. As of June 2026, two Claude models sit measurably above both on SWE-Bench Pro:

If your bottleneck is the hardest 10–20% of tasks — the cases where today’s escalation pattern is “the agent fails three times, then a senior engineer takes over” — neither M3 nor GPT-5.5 will move the needle on capability. The right route is Claude Opus 4.8 (better price-performance against GPT-5.5) or Claude Fable 5 (real capability ceiling, at 2× Opus pricing). We covered the three-way Claude / GPT comparison in the Fable 5 vs Opus 4.8 vs GPT-5.5 review, and the budget-end of the same stack in Claude Haiku 4 vs GPT-5.4 mini.

The realistic three-tier routing pattern that most teams will settle on by Q3 2026:

• Top tier (5–15% of traffic): Claude Fable 5 or Opus 4.8 — handed-off escalations, senior-engineer-level tasks
• Default tier (60–70%): MiniMax M3 — batch agents, long-context refactors, multimodal review
• Interactive tier (20–30%): GPT-5.5 (in Codex CLI / Cursor) — pair-programming, low-latency loops

The single biggest practical advantage of routing through ofox.ai is that all three tiers live behind the same OpenAI-compatible endpoint with the same billing — switching tier is a model-string change, not a vendor migration.

Try Both via ofox: A/B in 10 Lines of Code

Both minimax/minimax-m3 and openai/gpt-5.5 are live on the OpenAI-compatible endpoint at https://api.ofox.ai/v1. The model swap is one string. Here is the smallest useful A/B harness in Python and Node — run it on a representative chunk of your own workload before committing to a routing decision based on someone else’s benchmarks.

Python — A/B both models in one loop

from openai import OpenAI
import os, time

client = OpenAI(base_url="https://api.ofox.ai/v1", api_key=os.environ["OFOX_API_KEY"])

prompt = "Refactor this Python function to use async/await and return early on the empty-list case: ..."

for model in ["minimax/minimax-m3", "openai/gpt-5.5"]:
    t0 = time.time()
    resp = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
    )
    elapsed = time.time() - t0
    print(f"{model}: {elapsed:.1f}s, {resp.usage.total_tokens} tokens")
    print(resp.choices[0].message.content[:200])

That gives you raw latency, total token count, and a side-by-side of the actual output on your own task. The model ID is the only thing changing — same SDK, same endpoint, same auth. Swap the prompt for whatever your real workload looks like and run it across 20–30 representative cases.

Node — same shape

import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.ofox.ai/v1", apiKey: process.env.OFOX_API_KEY });

const prompt = "Refactor this Python function to use async/await and return early on the empty-list case: ...";

for (const model of ["minimax/minimax-m3", "openai/gpt-5.5"]) {
  const t0 = Date.now();
  const resp = await client.chat.completions.create({
    model,
    messages: [{ role: "user", content: prompt }],
  });
  console.log(`${model}: ${(Date.now() - t0) / 1000}s, ${resp.usage.total_tokens} tokens`);
  console.log(resp.choices[0].message.content.slice(0, 200));
}

MiniMax M3 multimodal — attach an image to the prompt

M3 is native multimodal; GPT-5.5 needs a separate vision call. Here is the M3-only path for diff screenshots or UI mockups in code review:

resp = client.chat.completions.create(
    model="minimax/minimax-m3",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Review this diff and call out any logic regressions."},
            {"type": "image_url", "image_url": {"url": "data:image/png;base64,<base64-PNG-here>"}},
        ],
    }],
)

The same code shape against openai/gpt-5.5 works for static images but requires an extra round-trip for video frames; M3 accepts video URLs directly. If multimodal code review is a meaningful slice of your workload, that round-trip difference adds up.

References

• ofox.ai model catalog
• VentureBeat — MiniMax-M3 debuts, eclipsing GPT-5.5
• Datanorth — MiniMax M3 specs and benchmarks
• MarkTechPost — MiniMax M3 release announcement
• [TechTimes — frontier claims, unverified benchmarks]
• Vellum — GPT-5.5 reference

At one-eleventh the cost per SWE-Bench Pro point, the question stopped being whether MiniMax M3 is “as good as” GPT-5.5 and started being which workload still justifies paying the GPT-5.5 premium — and “Codex CLI shop” is now the cleanest honest answer.