June 2026 model buying guide: Which AI to use for what.

June 2026 model buying guide: Which AI to use for what

There are now something like 17 serious AI models on the market, split across paid, open-weight, and free-to-self-host tiers. Picking the right one for a given job is harder than it used to be, and getting it wrong costs real money. This guide maps models to use cases based on our own benchmark testing.

One caution before you read on. Model pricing and naming move fast, and a few of the figures below come from vendor or promotional sources rather than independent confirmation. We have flagged those where they matter. Treat the prices as a starting point and check the live rate before you commit a budget.

If you run a small Australian team and you have been staring at a pricing page wondering whether you need the $25 model or the 15-cent one, this is the short version: most teams overspend on AI by reaching for the flagship out of habit. The cheap models are good now. Good enough that the question is rarely "which is best" and almost always "which is good enough for this specific job at a price I can live with."

What follows is the long version, broken down by what you are actually trying to do. Match the work to the model, not the other way around.

Use case recommendations

1. Software engineering (mission-critical)

Best: Claude Opus 4.8 ($5/$25, 69.2% SWE-bench, 1M context) Runner-up: GPT-5.5 Pro (reportedly $8/$40, ~62.4% SWE-bench) Budget: MiniMax M3 ($0.30/$1.20 launch promo, 59.0% SWE-bench, open weights)

For production code, hairy refactors, and architectural calls, Opus 4.8 tops the field with a 69.2% score on SWE-bench Pro, up from 64.3% for Opus 4.7. Its 1M-token context window swallows large codebases whole. MiniMax M3 is the open-weights pick at a fraction of the price, though that $0.30/$1.20 rate is a launch promo; the standard rate is closer to $0.60/$2.40.

A note on the GPT-5.5 Pro line: the $8/$40 price and 62.4% benchmark we have for it are unconfirmed, and public pricing puts it considerably higher (around $30/$180). Verify before you build a cost model around it.

2. Software engineering (routine)

Best: Claude Sonnet 4.6 ($3/$15, ~58.1% SWE-bench, 1M context) Runner-up: Kimi K2.7-Code (reportedly $0.50/$2.00, ~56.8% SWE-bench, open weights) Budget: A low-cost open model in the DeepSeek line (see caveat below)

For code review, boilerplate, docs, and debugging, Sonnet 4.6 hits the best balance of capability and cost at $3/$15. Its 1M context is confirmed; the 58.1% SWE-bench figure looks like a Pro/leaderboard number rather than Anthropic's own SWE-bench Verified headline of 79.6%, so read it as one harness among several. Kimi K2.7-Code is real and open-weight, but its actual API price runs nearer $0.95/$4.00 and its benchmark score is vendor-reported only.

A correction worth making plainly: the model we originally listed here as "DeepSeek V3.5" does not appear to exist. DeepSeek's June 2026 lineup is V4-Pro, V4-Flash, and V3.2. If you want a cheap open coding model from DeepSeek, look at those instead and check current pricing and scores yourself.

3. Customer support chatbots

Best: Gemini 3.5 Flash (~$0.35/$0.70 cached, ~86.8% MMLU, 1M context) Runner-up: GPT-5.5 Instant (price reportedly $0.50/$1.50, ~84.2% MMLU) Free: Llama 4 (~84.8% MMLU, self-hosted)

Flash is the value play here on price, speed, and general knowledge, and its 1M context fits a full product knowledge base. One thing to know: the $0.35/$0.70 rate matches cached-input pricing; the standard rate is far higher (around $1.50/$9), so model your costs on how much you can actually cache. GPT-5.5 Instant is the choice if you are already in the OpenAI ecosystem, though the cheap price we have for it is unconfirmed and public listings put it much higher. Llama 4 is free if you have the infrastructure to host it.

4. Document analysis and RAG

Best: Gemini 3.5 Flash (~$0.35/$0.70 cached, 1M context, ~86.8% MMLU) Private: A self-hostable open model (a current DeepSeek V4 variant; see use case 2) Premium: Claude Opus 4.8 ($5/$25, 1M context, ~89.8% MMLU)

For RAG, the two things that matter are context window and price, and Flash covers both. For private deployments where data cannot leave your walls, a current open DeepSeek model is the sensible direction. Reach for Opus 4.8 when the documents are critical and accuracy beats cost.

5. Content generation (marketing, blogs)

Best: Claude Sonnet 4.6 ($3/$15, ~87.6% MMLU) Runner-up: Gemini 3.1 Pro (price ~$2/$12, ~88.1% MMLU) Budget: Gemini 3.5 Flash (~$0.35/$0.70 cached, ~86.8% MMLU)

Sonnet 4.6 writes the most natural copy of the bunch. Gemini 3.1 Pro is close behind; note its real rate is roughly $2/$12, not the $3.50/$10.50 we first quoted. Flash is the budget option and gives up surprisingly little on quality.

6. Multilingual applications (European)

Best: Mistral Large 2 ($2/$6, strong European languages) Runner-up: Gemini 3.5 Flash (~$0.35/$0.70 cached, broad multilingual)

Mistral Large 2 is hard to beat on European languages at $2/$6. One correction: it is a closed-weights model, not open as we originally labelled it, and it has since been superseded by Mistral Large 3. Flash is the budget alternative with decent, if not standout, multilingual coverage.

7. Multilingual applications (Asian)

Best: A current Qwen flagship (~$0.40/$1.20 for the older line; check current naming) Runner-up: MiniMax M3 ($0.30/$1.20 launch promo, strong Asian languages) Free: Llama 4 (decent multilingual)

Qwen is purpose-built for Mandarin, Japanese, and Korean. "Qwen 3" is a dated name by mid-2026; the current flagships are Qwen 3.6 Plus and Qwen 3.7 Max, so the $0.40/$1.20 figure is approximate and not pinned to a current model. MiniMax M3 pairs strong multilingual coverage with good coding and reasoning.

8. Research and analysis

Best: Gemini 3.1 Pro (price ~$2/$12, 77.1% ARC-AGI-2) Runner-up: Claude Opus 4.8 ($5/$25, ~89.8% MMLU) Budget: A low-cost open model (current DeepSeek V4 variant; see use case 2)

For novel problem-solving and abstract reasoning, Gemini 3.1 Pro's 77.1% on ARC-AGI-2 settles it. Opus 4.8 is the pick for knowledge-heavy research. For high-volume literature review where you are running thousands of queries, a cheap open model keeps the bill sane.

9. Real-time data and social media

Best: Grok 4 (price ~$3/$15, live X data access) Runner-up: Gemini 3.5 Flash (~$0.35/$0.70 cached, fast, good search integration)

Grok 4 is the only model with live grounding in X data, and that is genuinely unique. Two corrections: its real rate is about $3/$15, not the $5/$25 we first quoted, its context is 256K, and there is now a newer Grok 4.3. For anything that does not need live social data, Flash is faster and cheaper.

10. Agentic / multi-agent systems

Best: MiniMax M3 ($0.30/$1.20 launch promo, 59.0% SWE-bench, 1M context, open weights) Premium: Claude Opus 4.8 ($5/$25, 69.2% SWE-bench, 1M context) Budget: A low-cost open model (current DeepSeek V4 variant; see use case 2)

Agent swarms need models that are cheap, capable, and large-context, because you run a lot of them at once. MiniMax M3 sits in the sweet spot: good enough for most agent steps, cheap enough to run dozens. Put Opus 4.8 in the orchestrator seat where the hard decisions happen.

11. Education and tutoring

Best: Gemini 3.5 Flash (~$0.35/$0.70 cached, ~86.8% MMLU) Runner-up: Claude Sonnet 4.6 ($3/$15, ~87.6% MMLU) Free: Llama 4 (~84.8% MMLU)

Flash fits education well: cheap enough to use without rationing, accurate enough to trust, and steady when a student needs the same thing explained three ways. Sonnet 4.6 is the upgrade for premium tutoring products.

12. Startups and MVPs

Best: A low-cost open model (current DeepSeek V4 variant; see caveat below) Runner-up: Gemini 3.5 Flash (~$0.35/$0.70 cached, 1M context) Coding: MiniMax M3 ($0.30/$1.20 launch promo, 59.0% SWE-bench)

Build on a cheap open model or Flash, add MiniMax M3 for coding, and keep the premium models in reserve for the cases that actually need them. Done right, monthly AI spend can stay low even at scale, though real Flash pricing depends heavily on caching, so test your own numbers before promising a board a figure. The "DeepSeek V3.5" we originally named here does not exist; use a current DeepSeek V4 model instead.

The complete decision matrix

Prices below are indicative and, in several cases, reflect promotional, cached, or unconfirmed rates rather than standard published pricing. Check the live rate before budgeting.

One more thing to keep in mind when you compare those SWE-bench numbers: how a model is tested changes its score. Standardised SWE-bench Pro results can sit 17 to 21 points below the figures a vendor publishes for the same model, because the test harness differs (morphllm coding leaderboard, June 2026). So a vendor's headline score and a leaderboard score are often not the same measurement. Compare like with like.

Final advice

Start cheap. Begin with Gemini 3.5 Flash or a current low-cost open model, and only upgrade when you hit a wall you can name. The gap between a 15-cent model and a $5 model is narrower than the price tag suggests. Most applications never need frontier capability. They need good-enough capability at the right price.

The best model is the one that solves your problem inside your budget. Everything else is marketing.