GPT-5.6 Luna vs MiniMax M3 vs DeepSeek V4 Flash: The Efficient-Tier Showdown
Three of the most cost-effective models available today — GPT-5.6 Luna ($0.20/$1.20), MiniMax M3 ($0.30/$1.20), and DeepSeek V4 Flash ($0.14/$0.28) — are all on Multos Lite and above. Luna matches Claude Opus 4.8 on GPQA Diamond and nearly ties Terra on coding — at 1/10th the price. Here's exactly when to use each one.
Three things happened recently that make this comparison timely. OpenAI cut GPT-5.6 Luna 80% — from $1/$6 to $0.20/$1.20 per million tokens. Luna moved to Lite tier on Multos, joining MiniMax M3 and DeepSeek V4 Flash as models available on every paid plan. At these price points all three overlap on enough use cases that picking wrong is a real cost. But there's a bigger story buried in the benchmarks: Luna nearly ties Terra on coding (74.6 vs 77.4 on AA Coding Agent Index), essentially matches it on Agents' Last Exam (50.3 vs 50.4%), and scores 92.3% on GPQA Diamond — matching Claude Opus 4.8 at $0.20/M input vs $5/M. This isn't a best-model ranking. It's a routing guide — and an argument for why Luna should be your default before you reach for Terra.
Pricing and Specs
GPT-5.6 Luna: $0.20 input / $1.20 output per million tokens. $0.02 cached input (10× discount). 1.05M context, 128K max output. Full OpenAI tool suite — web search, code interpreter, hosted shell, computer use, MCP, apply patch. Reasoning effort controls from none to max. Available via Responses API. MiniMax M3: $0.30 input / $1.20 output per million tokens. 1M context (512K guaranteed, 1M ceiling). 428B total parameters (~23B active per token, MoE). Native multimodal input — text, image, and video in the same call. Output is text only. Open weights on Hugging Face. MSA sparse attention. DeepSeek V4 Flash: $0.14 input / $0.28 output per million tokens. $0.014 cached input (10× discount). 1M context, 384K max output. 284B parameters, MIT licensed. 127 tokens/second — the fastest of the three by a wide margin.
Benchmark Comparison
Coding — SWE-bench Verified: DeepSeek V4 Flash: 79.0% | MiniMax M3: ~78% | GPT-5.6 Luna: 62.7% on SWE-bench Pro (harder variant), 74.6 on AA Coding Agent Index v1.1 LiveCodeBench (competitive programming): DeepSeek V4 Flash: 91.6% — the standout number. Strong pure-code benchmark. Artificial Analysis Intelligence Index: GPT-5.6 Luna: 51 | MiniMax M3: ~52 | DeepSeek V4 Flash: trails on abstract reasoning GPQA Diamond (graduate science reasoning): GPT-5.6 Luna: 92.3% — best of the three on knowledge-intensive tasks Terminal-Bench 2.1 (sustained multi-step agent): MiniMax M3: 66.0% — best of the three for long agentic runs GPT-5.6 Luna: strong with hosted shell and apply patch access DeepSeek V4 Flash: -11 points vs V4 Pro on complex terminal tasks — its clear weakness BrowseComp (hard web research): MiniMax M3: 83.5% (vendor-reported) | GPT-5.6 Luna: full BrowseComp tool access | DeepSeek V4 Flash: not built for deep research Speed: DeepSeek V4 Flash: ~127 tok/s | MiniMax M3: fast via MSA (9-15× faster than M2) | GPT-5.6 Luna: fastest in the GPT-5.6 family, ~80-100 tok/s
Luna vs the Frontier — The Number That Changes the Conversation
The benchmarks above compare Luna against M3 and Flash. But the more interesting comparison is Luna against models that cost 10-25× more. AA Coding Agent Index v1.1: GPT-5.6 Luna: 74.6 — ahead of Claude Opus 4.8 (72.5), behind Terra (77.4) and Sol (80) The gap between Luna and Terra on coding is 2.8 points. Terra costs 12.5× more per input token ($2.00 vs $0.20). For most coding workloads that gap is not worth 12.5×. Agents' Last Exam (real-world professional workflows): GPT-5.6 Luna: 50.3% — essentially tied with Terra (50.4%). Sol leads at 52.7%, but the Luna-to-Terra gap is 0.1 points. GPQA Diamond (graduate-level science reasoning): GPT-5.6 Luna: 92.3% Claude Opus 4.8: 92.0% — Luna matches a $5/M input model at $0.20/M Claude Fable 5: 92.6% — within 0.3 points GPT-5.6 Sol: 94.6% — Sol leads, but Luna is in the same neighbourhood Artificial Analysis Intelligence Index: GPT-5.6 Luna: 51 | MiniMax M3: ~52 | GPT-5.6 Terra: 55 | GPT-5.6 Sol: 59 | Claude Fable 5: ~61 Luna sits just below MiniMax M3 on general intelligence and well above Gemini 3.5 Flash and mid-tier open models. It's not a cheap model that happens to be available. It's a frontier-family model that got a dramatic price cut. The practical implication: before routing a task to Terra or Sol, ask whether the 2.8-point coding gap and 0.1-point professional-task gap justify the cost difference for that specific workload. For high-volume production tasks where you can afford an occasional retry, they usually don't.
Where Each Model Wins
Use GPT-5.6 Luna when the task needs the full OpenAI tool stack — hosted shell, computer use, MCP, web search, apply patch, structured outputs. Reasoning effort control matters here: dial low for quick tasks, push to high for tricky ones without swapping models. Luna also wins on professional reasoning — 92.3% GPQA Diamond is the highest of the three. Good default if you're already in the GPT-5.6 family and want to cut cost without leaving it. Use MiniMax M3 when you need native multimodal input — images and video alongside text in the same context. Best for sustained multi-hour agentic coding sessions (66.0% Terminal-Bench, 12+ hour demo run reproducing an ICLR paper). Visual debugging — pass a UI screenshot or a screen recording and ask what the user was doing. Open weights means you can self-host if needed. Best sustained intelligence ceiling of the three. Use DeepSeek V4 Flash when raw throughput and cost are the only constraints. 127 tok/s and $0.28/M output makes it the right default for classification, extraction, routing, structured data pipelines, and anything where you're sending millions of tokens per day. MIT license — no usage restrictions, no legal ambiguity for commercial products. Cache discounts are aggressive: 10× on cache hits.
Effective Cost at Production Cache Rates
At 85% prompt cache hit rate and 95:5 input/output ratio — the production averages Multos validates against: DeepSeek V4 Flash blended: ~$0.05/M — the current floor for frontier-capable AI GPT-5.6 Luna blended: ~$0.11/M — middle of the three MiniMax M3 blended: ~$0.14/M — highest of the three, justified by multimodal and agent ceiling For a 30M token month (Starter allocation): — DeepSeek V4 Flash: ~$1.50 effective — GPT-5.6 Luna: ~$3.30 effective — MiniMax M3: ~$4.20 effective The gap is real but narrow at these volumes. The main constraint for most Multos users is pool size, not per-token rate. Quality and task fit should drive the decision before price.
The One-Line Routing Rule
Flash for volume. M3 for long agents and visual input. Luna for tool-heavy or reasoning tasks. Auto mode on Multos routes across all three already — Flash and M3 for fast high-volume calls, Luna when the task needs OpenAI tooling or professional reasoning. Manual selection is for when you know the task type upfront. If you're building a product with an AI layer: use DeepSeek V4 Flash as the default path, MiniMax M3 when the task is a long agentic run or involves images and video, and Luna when you need hosted shell, computer use, or the task benefits from reasoning effort tuning. Run all three against your own evals. These benchmarks tell you the ceiling — your workload tells you the floor.
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All three models are live on Multos Lite and above. Pick from the model dropdown or let Auto Router decide.
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