Pricing Comparison: OpenAI vs. Mistral

OpenAI and Mistral have moved their AI token pricing in opposite directions over the past year. OpenAI's flagship input rate quadrupled. Mistral cut its open-weight flagship 75% and moved its premium positioning to a new model. Here is where both stand as of July 13, 2026.

OpenAI vs Mistral at a glance

Takeaway: Mistral undercuts OpenAI at every tier, with the widest gap on output rates. OpenAI sells depth of lineup and ecosystem.

Model overview

OpenAI's current lineup is the gpt-5.6 family, launched July 9, 2026. gpt-5.6-sol is the flagship at $5 / $30, gpt-5.6-terra the mid tier at $2.50 / $15, and gpt-5.6-luna the value pick within the family at $1 / $6. The gpt-5.5 and gpt-5.4 generations stay on the card, with gpt-5.5-pro as the premium reasoning option and gpt-5.4-nano as the cheapest model at $0.20 / $1.25.

Mistral splits its top end in two. Mistral Medium 3.5 is the performance flagship at $1.50 / $7.50, and Mistral Large 3 is the open-weight flagship at $0.50 / $1.50, so the naming and the pricing point in opposite directions. Mistral Small 4 at $0.15 / $0.60 is the value pick, with the Ministral 3 family at $0.10 to $0.20 flat for edge workloads, Devstral 2 for code, and Magistral Medium for reasoning.

Key features

Pricing snapshot

Prices verified July 13, 2026. Providers reprice often, see the update timeline below.

OpenAI API pricing

Standard tier, per 1M tokens, prompts up to 200k. Long-context rates where noted.

Batch API: roughly 50% off. Cached input: 10x cheaper than base input. Web search tool: $10 per 1k calls.

Mistral API pricing

Per 1M tokens.

Batch: 50% off. Cached input: 90% off.

Chat plans

What this costs at a real workload

The standardized scenario: 1,000 requests per day at roughly 1,000 input and 1,000 output tokens each, which works out to about 30M input and 30M output tokens per month. Monthly cost = 30 x input rate + 30 x output rate.

Tokenizer caveat for the Claude rows: Fable 5, Sonnet 5, and Opus 4.7+ use a new tokenizer that produces roughly 30% more tokens for the same text. Comparing per-token rates across providers understates Anthropic's effective cost per request by about that margin. Rate cards don't tell you what the invoice will say. The tokenizer does.

Mistral Medium 3.5 at $270 per month runs about a quarter of gpt-5.6-sol's $1,050, and Mistral Large 3 at $60 is cheaper than anything on OpenAI's card. Mistral Small 4 at $22.50 competes directly with high-volume workloads that would otherwise go to gpt-5.4-nano.

The spread across all six providers is 143x between the most and least expensive model for the same workload. The full 15-row table is on the AI Pricing Index. For how per-token rates turn into unit economics, see token economics.

Timeline of past updates

OpenAI

• July 9, 2026: gpt-5.6 family launch. Sol at $5 / $30, terra at $2.50 / $15, luna at $1 / $6

• April 24, 2026: gpt-5.5 launch at $5 / $30, doubling flagship input over gpt-5.4

• April 9, 2026: ChatGPT Pro $100 tier added below the $200 tier

• April 2, 2026: Team plan renamed Business, $25/user/mo

• March 5, 2026: gpt-5.4 family launch at $2.50 / $15, doubling over gpt-5

• August 2025: GPT-5 launch at $1.25 / $10

Trajectory: Flagship input went $1.25 to $2.50 to $5.00 in under a year. 4x.

Mistral

• April 29, 2026: Medium 3.5 at $1.50 / $7.50 becomes the performance flagship, priced above Large 3. The naming and the pricing now point in opposite directions

• March 16, 2026: Small 4 at $0.15 / $0.60

• 2026: Le Chat rebranded Vibe. Pro at $14.99, Team at $24.99

• December 2, 2025: Large 3 at $0.50 / $1.50, down from Large 24.11's $2 / $6. A 75% cut

Trajectory: Down on flagships, with the premium moving to Medium 3.5.

The AI Pricing Index

OpenAI logged 6 price events in 12 months, flagship up 4x. Mistral logged 4, with flagships down and the premium moving to Medium 3.5. A falling-price provider still forces the same billing work as a rising one: every cut re-prices your margin on every workload you route there.

The index counts documented list-price events per provider over the trailing 12 months, recounted monthly. Full methodology and all six provider timelines are on the AI Pricing Index.

What this means for your own pricing

If you build on OpenAI, Mistral, or both, this pricing complexity becomes your pricing complexity. Four problems show up regardless of which side you pick.

• The margin problem. Every user interaction has variable cost. A power user generating long responses on a flagship model costs 10-50x more than a casual user on a value model. Per-seat pricing doesn't see this. You need usage-aware billing to protect margins.

• The model mix problem. Products route different requests to different models, and the table above shows the cost range that creates inside a single product. Your billing system needs to know which model served which request and price accordingly.

• The credit translation problem. Many AI products abstract provider costs with credit-based pricing. That means maintaining a conversion layer from usage to credits to dollars. Every provider price event forces you to re-derive the conversion rates.

• The visibility problem. Finance needs margin by customer, by feature, by model, by month. If your metering system lives apart from your billing system, that reconciliation happens in spreadsheets.

The index above adds the repricing angle: these two providers logged 10 price events in 12 months between them. Each one forces you to re-derive your own margins, credit conversion rates, and FX exposure if your revenue and your model bill sit in different currencies. Solvimon runs that layer: metering per provider, credits and wallets as first-class primitives, rate cards you update without an engineering sprint. See Solvimon for AI.

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