Model pricing wars: June 2026 comparison table
Analysis
By the middle of 2026, the question that decides most AI buying calls isn't "which model is smartest." It's "which one is cheap enough to run all day without anyone wincing at the bill."
That shift happened fast. A couple of years ago the top models were genuinely far apart on quality, and you paid up for the best one because there wasn't a close substitute. Now the gap between a frontier model and a solid mid-tier one is small enough that, for a lot of everyday work, the cheaper option just does the job. So vendors compete on the one lever left: price.
The numbers have moved a long way. A model that would have counted as frontier-grade in 2024 now runs for less than a dollar per million tokens. For a business team, that's the headline: the floor has dropped, and most of what you want to do sits comfortably above it.
One caution before the table. AI pricing changes weekly, vendors run promo rates, and "the same model" can mean different SKUs at different prices. We checked these figures against public pricing trackers in June 2026. Some line up exactly. Several don't, and we've said so directly rather than passing them off as gospel.
Complete pricing table
| Model | Input / 1M | Output / 1M | Combined* | SWE-bench | MMLU | Context |
|---|---|---|---|---|---|---|
| Llama 4 | Free | Free | Free | 50.2% | 84.8% | 256K |
| DeepSeek V3.5 | $0.15 | $0.60 | $1.35 | 52.4% | 85.8% | 1M |
| Gemini 3.5 Flash | $0.35 | $0.70 | $1.75 | 48.2% | 86.8% | 1M |
| Qwen 3 | $0.40 | $1.20 | $2.80 | 46.2% | 84.6% | 128K |
| GPT-5.5 Instant | $0.50 | $1.50 | $3.50 | 42.1% | 84.2% | 128K |
| MiniMax M3 | $0.30 | $1.20 | $2.70 | 59.0% | 86.4% | 1M |
| Kimi K2.7-Code | $0.50 | $2.00 | $5.00 | 56.8% | 85.7% | 256K |
| GLM-5.2 | $0.80 | $2.40 | $5.60 | 51.4% | 85.2% | 256K |
| Mistral Large 2 | $2.00 | $6.00 | $14.00 | 48.6% | 85.1% | 256K |
| Gemini 3.1 Pro | $3.50 | $10.50 | $24.50 | 54.2% | 88.1% | 1M |
| Claude Sonnet 4.6 | $3.00 | $15.00 | $33.00 | 58.1% | 87.6% | 1M |
| Claude Opus 4.8 | $5.00 | $25.00 | $55.00 | 69.2% | 89.8% | 1M |
| Claude Opus 4.7 | $5.00 | $25.00 | $55.00 | 63.8% | 89.2% | 1M |
| Grok 4 | $5.00 | $25.00 | $55.00 | 54.8% | 87.2% | 256K |
| GPT-5.5 | $5.00 | $30.00 | $65.00 | 58.6% | 88.4% | 400K |
| GPT-5.5 Pro | $8.00 | $40.00 | $88.00 | 62.4% | 89.7% | 400K |
| Claude Fable 5 | $10.00 | $50.00 | $110.00 | 80.3% | 92.1% | 1M |
*Combined = 1M input + 2M output tokens (typical assistant workload). The "SWE-bench" column reflects SWE-bench Pro figures, not Verified, worth knowing before you compare these against scores you've seen elsewhere.
What checks out, and what doesn't
Before you build a budget on this, here's where the figures stand against public pricing as of June 2026:
- Confirmed: Claude Opus 4.8 at $5/$25 input/output (CloudZero (opens in a new tab)), with a 69.2% SWE-bench Pro score (Morph LLM leaderboard (opens in a new tab)). Claude Opus 4.7 at $5/$25 (Finout (opens in a new tab)). Claude Sonnet 4.6 (opens in a new tab) at $3/$15 with a 1M context. Claude Fable 5 (opens in a new tab) at $10/$50 and 80.3% SWE-bench Pro. GPT-5.5 standard at $5/$30 (AI Pricing Guru (opens in a new tab)). Mistral Large 2 at $2/$6 (AI Pricing Guru (opens in a new tab)). MiniMax M3 at roughly $0.30/$1.20 on its promo rate, 59% SWE-bench Pro, 1M context (VentureBeat (opens in a new tab)).
- Roughly right, with caveats: Opus 4.7's SWE-bench Pro is reported closer to 64.3% than 63.8% (Vellum (opens in a new tab)). Qwen 3's $0.40/$1.20 matches the Qwen-Plus mid tier, not "Qwen 3" as one SKU (eesel AI (opens in a new tab)). Llama 4 is genuinely free to run on your own hardware (you still pay for the GPUs).
- Wrong or unconfirmed, don't budget off these: GPT-5.5 Pro is listed at $8/$40, but actual pricing reportedly runs $30/$180 (PricePerToken (opens in a new tab)). Gemini 3.5 Flash is shown at $0.35/$0.70; reported launch pricing is closer to $1.50/$9.00 (DevTk (opens in a new tab)). Gemini 3.1 Pro is listed at $3.50/$10.50, with reported figures nearer $2.00/$12.00 (DevTk (opens in a new tab)). Grok 4 is shown at $5/$25, reportedly closer to $3/$15 (PricePerToken (opens in a new tab)). Kimi K2.7-Code is listed at $0.50/$2.00, reportedly $0.95/$4.00 (TokenCost (opens in a new tab)). GLM-5.2 is shown at $0.80/$2.40 with 256K context; reported figures are $1.40/$4.40 with a 1M window (CloudPrice (opens in a new tab)). GPT-5.5's context is listed as 400K but is reportedly nearer 1M (Skywork (opens in a new tab)). A distinct "DeepSeek V3.5" and a separate "GPT-5.5 Instant" SKU at the prices shown could not be confirmed against current pricing pages.
Price-performance tiers
Read these tiers as the shape of the market, not as fixed quotes. The bands hold up even where individual cells don't.
Free tier: Llama 4. You pay for infrastructure, not tokens. Best if you're self-hosting on GPUs you already own.
Ultra-budget ($1-3): DeepSeek V3.5, Gemini 3.5 Flash, MiniMax M3. Capable models at very low list prices. On the figures shown, DeepSeek wins on input, Flash on output, and MiniMax on raw capability, though, as flagged above, the DeepSeek and Flash numbers here are the unconfirmed ones, so treat that ranking loosely.
Budget ($3-6): Qwen 3, GPT-5.5 Instant, Kimi K2.7-Code. Each has a lane. Qwen for multilingual work, the Instant tier for teams already in the OpenAI ecosystem, Kimi for coding.
Mid-range ($6-15): GLM-5.2, Mistral Large 2. Premium open-weight models with specific strengths, GLM leans on knowledge tasks, Mistral on European languages.
Premium ($15-35): Gemini 3.1 Pro, Sonnet 4.6. Strong closed models, both with 1M-token contexts.
Ultra-premium ($55+): Opus 4.8, Grok 4, GPT-5.5, GPT-5.5 Pro, Fable 5. Top capability, top price. You go here when the work warrants it, not by default.
The pricing trend
Prices are dropping faster than capabilities are climbing. A model scoring 85%+ on MMLU and 50%+ on SWE-bench Pro, frontier territory in 2024, now runs for under a dollar per million tokens. MiniMax M3 is the cleanest example: 86.4% MMLU, 59% SWE-bench Pro, and a promo rate in the $0.30-$1.20 range (VentureBeat (opens in a new tab)). That kind of compression is what's pulling AI into everyday business workflows at scale.
Verdict
The price war is good news if you're buying. The headline gap looks enormous, the cheapest input rate in the table is a fraction of the priciest, but the capability gap is nowhere near that wide. (And the most extreme version of that comparison leans on the DeepSeek figure, which is one of the unconfirmed ones, so don't quote a precise multiple.)
The practical takeaway holds regardless: for most jobs, a model in the sub-$1.20 range will do the work. Keep the ultra-premium models for the tasks where a wrong answer is genuinely expensive, and before you commit a budget, check current vendor pricing yourself, because the figures move and a few in this table are off.
Model pricing wars: answer-first summary
Model pricing wars matters because it can change how Founders and operators plan, build, or govern an tool evaluation workflow. A full price comparison of 17 models.
The direct answer is this: do not treat the topic as a standalone trend. Treat it as a decision about inputs, outputs, review ownership, data exposure, and whether the workflow produces a result that is faster, safer, or more useful than the current process.
Model pricing wars: implementation checklist
- Define the user, job to be done, and success metric for the tool evaluation workflow.
- Collect real examples, policies, source files, customer questions, or search queries before writing prompts or choosing tools.
- Separate low-risk drafts from decisions that need approval, privacy checks, or senior review.
- Document what the AI is allowed to access, what it must not access, and who signs off before production use.
- Review time to value, adoption rate, cost per workflow, quality review score after a small pilot rather than judging the idea from a demo.
This keeps the work practical. It also gives search engines and AI answer engines a clean factual structure: what the topic is, who it helps, what to do next, and which risks matter before implementation.
Decision criteria for Model pricing wars
| Decision area | What to check | Production signal |
|---|---|---|
| Intent | Does Model pricing wars solve a real workflow problem? | The use case has a named owner and measurable outcome. |
| Data | Can the required data be used safely? | Sensitive data is classified and access is controlled. |
| Quality | Can a reviewer judge the output consistently? | Examples, rubrics, or acceptance criteria exist. |
| Scale | Can the workflow be repeated without hero effort? | The process is documented and can be handed to another team member. |
Practical example for Model pricing wars
A small business could use this article to choose one practical test. For example, a manager might take one customer-facing process, one internal document workflow, or one recurring content task and redesign only that step with AI support. The goal is not to automate the whole business at once; it is to learn where Model Review creates reliable leverage.
The useful deliverable is a short operating note: the trigger, the source material, the prompt or tool, the review checklist, the escalation rule, and the metric. That note becomes the handover asset for staff training, SEO/GEO content, service delivery, or future agent work.
Risks and controls for Model pricing wars
The common failure pattern is moving too quickly from a promising idea into an unmanaged workflow. For Model pricing wars, the risk is not only bad output. It can also be unclear data permission, staff confusion, duplicate content, unreviewed customer advice, or a tool that quietly changes cost or capability.
- Control tool sprawl with a named owner, a review step, and written acceptance criteria.
- Control unclear pricing with a named owner, a review step, and written acceptance criteria.
- Control vendor lock-in with a named owner, a review step, and written acceptance criteria.
- Control unreviewed data sharing with a named owner, a review step, and written acceptance criteria.
Measurement plan for Model pricing wars
A useful AI or SEO initiative should leave evidence. Track time to value, adoption rate, cost per workflow, quality review score and compare the pilot against the current process. If the measure does not improve, keep the learning but avoid scaling the workflow.
For GEO readiness, the page should also answer the core question directly, define the entities involved, include implementation steps, explain tradeoffs, and link readers to the next relevant AI Kick Start service, guide, tool, or article.
Definitions and entities for Model pricing wars
For search, GEO, and staff handover, define the core entities in plain language. In this article the important entities are the workflow owner, the AI tool or model, the source material, the review process, the risk boundary, and the measurable business outcome. Clear definitions make the page easier for people to scan and easier for AI answer engines to quote accurately.
- Workflow owner: the person accountable for deciding whether Model pricing wars belongs in the business process.
- Source material: the documents, examples, policies, URLs, prompts, videos, or customer questions that ground the output.
- Review boundary: the point where a human checks accuracy, privacy, brand voice, or customer impact before the result is used.
- Success metric: the measure that proves whether the tool evaluation workflow is worth repeating.
Model pricing wars versus doing nothing
Doing nothing is also a decision. The cost may be slow manual work, weaker search visibility, inconsistent advice, duplicated effort, or staff using unmanaged AI tools without a shared process. The practical question is whether a controlled pilot can reduce that cost without creating a larger governance problem.
| Option | When it makes sense | What to watch |
|---|---|---|
| Do nothing | The workflow is rare, low value, or already reliable. | Competitors may improve speed, content depth, or service consistency first. |
| Run a small pilot | The task repeats often and has clear review criteria. | Keep scope tight and measure the result against the current process. |
| Build a production workflow | The pilot is repeatable and risk controls are documented. | Assign ownership, monitoring, training, and a rollback path. |
AI Kick Start handover package for Model pricing wars
A production handover should be concrete enough that another person can run it. For Model pricing wars, that means a short brief, a workflow map, approved prompts or tool settings, source material, a review checklist, internal links to supporting resources, and a simple measurement sheet. This is the difference between reading about AI and turning it into operational capability.
That packaging also strengthens E-E-A-T. It shows experience through implementation notes, expertise through decision criteria, authoritativeness through source-aware structure, and trust through risks, controls, and review steps. The article becomes useful even if the reader never buys a tool because it helps them make a better operational decision.
